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Journal articles on the topic 'Unconstrained face recognition'

Author: Grafiati

Published: 4 May 2024

Last updated: 31 July 2025

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1

Deng, Weihong, Jiani Hu, Zhongjun Wu, and Jun Guo. "Lighting-aware face frontalization for unconstrained face recognition." Pattern Recognition 68 (August 2017): 260–71. http://dx.doi.org/10.1016/j.patcog.2017.03.024.

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2

Masi, Iacopo, Anh Tuấn Trần, Tal Hassner, Gozde Sahin, and Gérard Medioni. "Face-Specific Data Augmentation for Unconstrained Face Recognition." International Journal of Computer Vision 127, no. 6-7 (2019): 642–67. http://dx.doi.org/10.1007/s11263-019-01178-0.

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3

Tyagi, Ranbeer, Geetam Singh Tomar, and Laxmi Shrivastava. "Unconstrained Face Recognition Quality: A Review." International Journal of Signal Processing, Image Processing and Pattern Recognition 9, no. 11 (2016): 199–210. http://dx.doi.org/10.14257/ijsip.2016.9.11.18.

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4

Vinay, A., Abhijay Gupta, Aprameya Bharadwaj, Arvind Srinivasan, K. N. Balasubramanya Murthy, and S. Natarajan. "Unconstrained Face Recognition using Bayesian Classification." Procedia Computer Science 143 (2018): 519–27. http://dx.doi.org/10.1016/j.procs.2018.10.425.

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5

Rifaee, Mustafa, Mohammad Al Rawajbeh, Basem AlOkosh, and Farhan AbdelFattah. "A New approach to Recognize Human Face Under Unconstrained Environment." International Journal of Advances in Soft Computing and its Applications 14, no. 2 (2022): 2–13. http://dx.doi.org/10.15849/ijasca.220720.01.

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Human face is considered as one of the most useful traits in biometrics, and it has been widely used in education, security, military and many other applications. However, in most of currently deployed face recognition systems ideal imaging conditions are assumed; to capture a fully featured images with enough quality to perform the recognition process. As the unmasked face will have a considerable impact on the numbers of new infections in the era of COVID-19 pandemic, a new unconstrained partial facial recognition method must be developed. In this research we proposed a mask detection method

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Yu, Aihua, Gang Li, Beiping Hou, Hongan Wang, and Gaoya Zhou. "A novel framework for face recognition using robust local representation–based classification." International Journal of Distributed Sensor Networks 15, no. 3 (2019): 155014771983608. http://dx.doi.org/10.1177/1550147719836082.

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Face recognition via representation-based classification is a trending technique in the recent years. However, the recognition performance of the systems using such a technique degrades in an unconstrained environment. In this article, a novel framework is proposed for representation-based face recognition. To deal with the unconstrained environment, a pre-process is used to frontalize face images, and aligned downsampling local binary pattern features of the frontalized images are used for classification. A dimension reduction is then adopted in order to reduce the computation complexity via

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TORBATI, Ali, and Önsen TOYGAR. "MASKED AND UNMASKED FACE RECOGNITION ON UNCONSTRAINED FACIAL IMAGES USING HAND-CRAFTED METHODS." Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 26, Özel Sayı (2023): 1133–39. http://dx.doi.org/10.17780/ksujes.1339868.

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In this study, the face recognition task is applied on masked and unmasked faces using hand-crafted methods. Due to COVID-19 and masks, facial identification from unconstrained images became a hot topic. To avoid COVID-19, most people use masks outside. In many cases, typical facial recognition technology is useless. The majority of contemporary advanced face recognition methods are based on deep learning, which primarily relies on a huge number of training examples, however, masked face recognition may be investigated using hand-crafted approaches at a lower computing cost than using deep lea

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Ruan, Shuai, Chaowei Tang, Xu Zhou, et al. "Multi-Pose Face Recognition Based on Deep Learning in Unconstrained Scene." Applied Sciences 10, no. 13 (2020): 4669. http://dx.doi.org/10.3390/app10134669.

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At present, deep learning drives the rapid development of face recognition. However, in the unconstrained scenario, the change of facial posture has a great impact on face recognition. Moreover, the current model still has some shortcomings in accuracy and robustness. The existing research has formulated two methods to solve the above problems. One method is to model and train each pose separately. Then, a fusion decision will be made. The other method is to make “frontal” faces on the image or feature level and transform them into “frontal” face recognition. Based on the second idea, we propo

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9

Tong, Ying, Jiachao Zhang, and Rui Chen. "Discriminative Sparsity Graph Embedding for Unconstrained Face Recognition." Electronics 8, no. 5 (2019): 503. http://dx.doi.org/10.3390/electronics8050503.

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In this paper, we propose a new dimensionality reduction method named Discriminative Sparsity Graph Embedding (DSGE) which considers the local structure information and the global distribution information simultaneously. Firstly, we adopt the intra-class compactness constraint to automatically construct the intrinsic adjacent graph, which enhances the reconstruction relationship between the given sample and the non-neighbor samples with the same class. Meanwhile, the inter-class compactness constraint is exploited to construct the penalty adjacent graph, which reduces the reconstruction influe

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10

Agrawal, Amrit Kumar, and Yogendra Narain Singh. "Unconstrained face recognition using deep convolution neural network." International Journal of Information and Computer Security 12, no. 2/3 (2020): 332. http://dx.doi.org/10.1504/ijics.2020.10026788.

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Agrawal, Amrit Kumar, and Yogendra Narain Singh. "Unconstrained face recognition using deep convolution neural network." International Journal of Information and Computer Security 12, no. 2/3 (2020): 332. http://dx.doi.org/10.1504/ijics.2020.105183.

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12

Agrawal, Amrit Kumar, and Yogendra Narain Singh. "Evaluation of Face Recognition Methods in Unconstrained Environments." Procedia Computer Science 48 (2015): 644–51. http://dx.doi.org/10.1016/j.procs.2015.04.147.

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13

Zhang, Monica M. Y., Kun Shang, and Huaming Wu. "Deep compact discriminative representation for unconstrained face recognition." Signal Processing: Image Communication 75 (July 2019): 118–27. http://dx.doi.org/10.1016/j.image.2019.03.015.

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14

Tyagi, Ranbeer. "Reference Face Based Technique for Unconstrained Face Recognition from Images Gallery." International Journal of Computer Graphics 10, no. 1 (2019): 1–16. http://dx.doi.org/10.21742/ijcg.2019.10.1.01.

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15

Khalifa, Aly, Ahmed A. Abdelrahman, Dominykas Strazdas, Jan Hintz, Thorsten Hempel, and Ayoub Al-Hamadi. "Face Recognition and Tracking Framework for Human–Robot Interaction." Applied Sciences 12, no. 11 (2022): 5568. http://dx.doi.org/10.3390/app12115568.

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Recently, face recognition became a key element in social cognition which is used in various applications including human–robot interaction (HRI), pedestrian identification, and surveillance systems. Deep convolutional neural networks (CNNs) have achieved notable progress in recognizing faces. However, achieving accurate and real-time face recognition is still a challenging problem, especially in unconstrained environments due to occlusion, lighting conditions, and the diversity in head poses. In this paper, we present a robust face recognition and tracking framework in unconstrained settings.

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16

Moghekar, Rajeshwar, and Sachin Ahuja. "Deep Learning Model for Face Recognition in Unconstrained Environment." Journal of Computational and Theoretical Nanoscience 16, no. 10 (2019): 4309–12. http://dx.doi.org/10.1166/jctn.2019.8518.

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Face recognition from videos is challenging problem as the face image captured has variations in terms of pose, Occlusion, blur and resolution. It has many applications including security monitoring and authentication. A subset of Indian Movies Face database (IMFDB) which has collection of face images retrieved from movie/video of actors which vary in terms of blur, pose, noise and illumination is used in our work. Our work focuses on the use of pre-trained deep learning models and applies transfer learning to the features extracted from the CNN layers. Later we compare it Fine tuned model. Th

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17

Borovikov, Eugene, Szilard Vajda, and Michael Gill. "Face Match for Family Reunification." International Journal of Computer Vision and Image Processing 7, no. 2 (2017): 19–35. http://dx.doi.org/10.4018/ijcvip.2017040102.

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Despite the many advances in face recognition technology, practical face detection and matching for unconstrained images remain challenging. A real-world Face Image Retrieval (FIR) system is described in this paper. It is based on optimally weighted image descriptor ensemble utilized in single-image-per-person (SIPP) approach that works with large unconstrained digital photo collections. The described visual search can be deployed in many applications, e.g. person location in post-disaster scenarios, helping families reunite quicker. It provides efficient means for face detection, matching and

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18

M. P, Milan. "CHALLENGES IN FACE RECOGNITION TECHNIQUE." Journal of University of Shanghai for Science and Technology 23, no. 07 (2021): 1201–4. http://dx.doi.org/10.51201/jusst/21/07253.

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Face detection is an application that is able of detecting, track, and recognizing human faces from an angle or video captured by a camera. A lot of advances have been made up in the domain of face recognition for security, identification, and appearance purpose, but still, difficult to able to beat humans alike accuracy. There are various problems in human facial presence such as; lighting conditions, image noise, scale, presentation, etc. Unconstrained face detection remains a difficult problem due to intra-class variations acquired by occlusion, disguise, capricious orientations, facial exp

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19

Ramos-Cooper, Solange, Erick Gomez-Nieto, and Guillermo Camara-Chavez. "VGGFace-Ear: An Extended Dataset for Unconstrained Ear Recognition." Sensors 22, no. 5 (2022): 1752. http://dx.doi.org/10.3390/s22051752.

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Recognition using ear images has been an active field of research in recent years. Besides faces and fingerprints, ears have a unique structure to identify people and can be captured from a distance, contactless, and without the subject’s cooperation. Therefore, it represents an appealing choice for building surveillance, forensic, and security applications. However, many techniques used in those applications—e.g., convolutional neural networks (CNN)—usually demand large-scale datasets for training. This research work introduces a new dataset of ear images taken under uncontrolled conditions t

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20

Zheng, Jingxiao, Rajeev Ranjan, Ching-Hui Chen, Jun-Cheng Chen, Carlos D. Castillo, and Rama Chellappa. "An Automatic System for Unconstrained Video-Based Face Recognition." IEEE Transactions on Biometrics, Behavior, and Identity Science 2, no. 3 (2020): 194–209. http://dx.doi.org/10.1109/tbiom.2020.2973504.

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21

Zhao, Jian, Lin Xiong, Jianshu Li, Junliang Xing, Shuicheng Yan, and Jiashi Feng. "3D-Aided Dual-Agent GANs for Unconstrained Face Recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 41, no. 10 (2019): 2380–94. http://dx.doi.org/10.1109/tpami.2018.2858819.

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22

Chen, Yi-Chen, Vishal M. Patel, P. Jonathon Phillips, and Rama Chellappa. "Dictionary-Based Face and Person Recognition From Unconstrained Video." IEEE Access 3 (2015): 1783–98. http://dx.doi.org/10.1109/access.2015.2485400.

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23

Selvi, Murugesan Chengathir, and Karuppiah Muneeswaran. "Unconstrained face recognition in surveillance videos using moment invariants." International Journal of Biomedical Engineering and Technology 25, no. 2/3/4 (2017): 282. http://dx.doi.org/10.1504/ijbet.2017.087729.

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24

Selvi, Murugesan Chengathir, and Karuppiah Muneeswaran. "Unconstrained face recognition in surveillance videos using moment invariants." International Journal of Biomedical Engineering and Technology 25, no. 2/3/4 (2017): 282. http://dx.doi.org/10.1504/ijbet.2017.10008626.

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25

., Anubha Pearline S. "FACE RECOGNITION UNDER VARYING BLUR IN AN UNCONSTRAINED ENVIRONMENT." International Journal of Research in Engineering and Technology 05, no. 04 (2016): 376–81. http://dx.doi.org/10.15623/ijret.2016.0504070.

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26

Gao, Yongbin, and Hyo Jong Lee. "Learning warps based similarity for pose-unconstrained face recognition." Multimedia Tools and Applications 77, no. 2 (2017): 1927–42. http://dx.doi.org/10.1007/s11042-017-4359-9.

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27

Sharma, Poonam. "Face recognition under unconstrained environment for videos from internet." CSI Transactions on ICT 8, no. 2 (2020): 241–48. http://dx.doi.org/10.1007/s40012-020-00302-7.

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28

Lv, Jiang-Jing, Cheng Cheng, Guo-Dong Tian, Xiang-Dong Zhou, and Xi Zhou. "Landmark perturbation-based data augmentation for unconstrained face recognition." Signal Processing: Image Communication 47 (September 2016): 465–75. http://dx.doi.org/10.1016/j.image.2016.03.011.

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29

Beham, M. Parisa, S. M. Mansoor Roomi, J. Alageshan, and V. Kapileshwaran. "Performance Analysis of Pose Invariant Face Recognition Approaches in Unconstrained Environments." International Journal of Computer Vision and Image Processing 5, no. 1 (2015): 66–81. http://dx.doi.org/10.4018/ijcvip.2015010104.

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Face recognition and authentication are two significant and dynamic research issues in computer vision applications. There are many factors that should be accounted for face recognition; among them pose variation is a major challenge which severely influence in the performance of face recognition. In order to improve the performance, several research methods have been developed to perform the face recognition process with pose invariant conditions in constrained and unconstrained environments. In this paper, the authors analyzed the performance of a popular texture descriptors viz., Local Bina

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30

Haghighat, Mohammad, Mohamed Abdel-Mottaleb, and Wadee Alhalabi. "Fully automatic face normalization and single sample face recognition in unconstrained environments." Expert Systems with Applications 47 (April 2016): 23–34. http://dx.doi.org/10.1016/j.eswa.2015.10.047.

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31

Zhuang, Weiwei, Liang Chen, Chaoqun Hong, Yuxin Liang, and Keshou Wu. "FT-GAN: Face Transformation with Key Points Alignment for Pose-Invariant Face Recognition." Electronics 8, no. 7 (2019): 807. http://dx.doi.org/10.3390/electronics8070807.

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Face recognition has been comprehensively studied. However, face recognition in the wild still suffers from unconstrained face directions. Frontal face synthesis is a popular solution, but some facial features are missed after synthesis. This paper presents a novel method for pose-invariant face recognition. It is based on face transformation with key points alignment based on generative adversarial networks (FT-GAN). In this method, we introduce CycleGAN for pixel transformation to achieve coarse face transformation results, and these results are refined by key point alignment. In this way, f

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32

Kakula, Francis, Jimmy Mbelwa, and Hellen Maziku. "Advancing Face Recognition Technologies: The Role of Decision Trees in Classifying Complex Image Pairs." Journal of ICT Systems 2, no. 2 (2024): 29–41. http://dx.doi.org/10.56279/jicts.v2i2.89.

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The advancement of face recognition technologies has been pivotal in various applications, from security systems to personalized user experiences. There are significant efforts already devoted to solving challenges of multimodality and pose variation in face recognition. Some studies focus on multimodality but pose-invariant, and other studies focus on pose variation but single modality. Despite significant progress, various face recognition algorithms do not consider both multimodality and pose variation constraints in their proposed methods. Recognizing face images presented both in a differ

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33

Amjed, Noor, Fatimah Khalid, Rahmita Wirza O. K. Rahmat, and Hizmawati Bint Madzin. "A Robust Geometric Skin Colour Face Detection Method under Unconstrained Environment of Smartphone Database." Applied Mechanics and Materials 892 (June 2019): 31–37. http://dx.doi.org/10.4028/www.scientific.net/amm.892.31.

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Face detection is the primary task in building a vision-based human-computer interaction system and in special applications such as face recognition, face tracking, face identification, expression recognition and also content-based image retrieval. A potent face detection system must be able to detect faces irrespective of illuminations, shadows, cluttered backgrounds, orientation and facial expressions. In previous literature, many approaches for face detection had been proposed. However, face detection in outdoor images with uncontrolled illumination and images with complex background are st

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34

Siddique, Muhammad Tariq, Ibrahim Venkat, Humera Farooq, Sharul Tajuddin, and S. H. Shah Newaz. "Virtual sample based techniques using deep features for SSPP face recognition in unconstrained environment." PLOS One 20, no. 5 (2025): e0322638. https://doi.org/10.1371/journal.pone.0322638.

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As challenging as it is to use face recognition with a Single Sample Per Person, it becomes even more difficult when face recognition based on a single sample is performed in an unconstrained environment. The unconstrained environment is normally considered irregular in facial expressions, pose, occlusion, and illumination. This degree of difficulty increases as a result of the single sample and in the presence of occlusion. Extensive research has been done on face recognition under pose and expression changes. Comparatively, less research has been reported on the occlusion problem that occurs

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35

Nachet, Randa, and Tarik Boudghene Stambouli. "Enhanced face recognition in unconstrained environments: leveraging 3D morphable model and focal modulation network." Brazilian Journal of Technology 8, no. 1 (2025): e76547. https://doi.org/10.38152/bjtv8n1-007.

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Due to the fact that faces can vary significantly in appearance when viewed from different angles or orientations, face recognition systems can easily be impacted by this issue. In this paper, we propose a novel approach for face frontalization leveraging a 3D morphable model (3DMM) and advanced deep learning techniques. Our architecture comprises two main modules: the 3D face fitting module, where we redesign this strategy using a focal modulation network-based encoder, and the texture completion module, which recovers the unseen regions caused by self-occlusion. Experimental results demonstr

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36

Wang, Dongshu, Heshan Wang, Jiwen Sun, Jianbin Xin, and Yong Luo. "Face Recognition in Complex Unconstrained Environment with An Enhanced WWN Algorithm." Journal of Intelligent Systems 30, no. 1 (2020): 18–39. http://dx.doi.org/10.1515/jisys-2019-0114.

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Abstract Face recognition is one of the core and challenging issues in computer vision field. Compared to computer vision, human visual system can identify a target from complex backgrounds quickly and accurately. This paper proposes a new network model deriving from Where-What Networks (WWNs), which can approximately simulate the information processing pathways (i.e., dorsal pathway and ventral pathway) of human visual cortex and recognize different types of faces with different locations and sizes in complex background. To enhance the recognition performance, synapse maintenance mechanism an

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37

S., Yallamandaiah, and Purnachand N. "An effective face recognition method using guided image filter and convolutional neural network." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (2021): 1699. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1699-1707.

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<p>In the area of computer vision, face recognition is a challenging task because of the pose, facial expression, and illumination variations. The performance of face recognition systems reduces in an unconstrained environment. In this work, a new face recognition approach is proposed using a guided image filter, and a convolutional neural network (CNN). The guided image filter is a smoothing operator and performs well near the edges. Initially, the ViolaJones algorithm is used to detect the face region and then smoothened by a guided image filter. Later the proposed CNN is used to extra

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S., Yallamandaiah, and Purnachand N. "An effective face recognition method using guided image filter and convolutional neural network." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (2021): 1699–707. https://doi.org/10.11591/ijeecs.v23.i3.pp1699-1707.

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In the area of computer vision, face recognition is a challenging task because of the pose, facial expression, and illumination variations. The performance of face recognition systems reduces in an unconstrained environment. In this work, a new face recognition approach is proposed using a guided image filter, and a convolutional neural network (CNN). The guided image filter is a smoothing operator and performs well near the edges. Initially, the ViolaJones algorithm is used to detect the face region and then smoothened by a guided image filter. Later the proposed CNN is used to extract the fe

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39

Lakshmi, Napa, and Megha P. Arakeri. "A novel sketch based face recognition in unconstrained video for criminal investigation." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 1499. http://dx.doi.org/10.11591/ijece.v13i2.pp1499-1509.

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Face recognition in video surveillance helps to identify an individual by comparing facial features of given photograph or sketch with a video for criminal investigations. Generally, face sketch is used by the police when suspect’s photo is not available. Manual matching of facial sketch with suspect’s image in a long video is tedious and time-consuming task. To overcome these drawbacks, this paper proposes an accurate face recognition technique to recognize a person based on his sketch in an unconstrained video surveillance. In the proposed method, surveillance video and sketch of suspect is

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40

Napa, Lakshmi, and P. Arakeri Megha. "A novel sketch based face recognition in unconstrained video for criminal investigation." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 1499–509. https://doi.org/10.11591/ijece.v13i2.pp1499-1509.

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Face recognition in video surveillance helps to identify an individual by comparing facial features of given photograph or sketch with a video for criminal investigations. Generally, face sketch is used by the police when suspect’s photo is not available. Manual matching of facial sketch with suspect’s image in a long video is tedious and time-consuming task. To overcome these drawbacks, this paper proposes an accurate face recognition technique to recognize a person based on his sketch in an unconstrained video surveillance. In the proposed method, surveillance video and sketch of

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41

BARR, JEREMIAH R., KEVIN W. BOWYER, PATRICK J. FLYNN, and SOMA BISWAS. "FACE RECOGNITION FROM VIDEO: A REVIEW." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 05 (2012): 1266002. http://dx.doi.org/10.1142/s0218001412660024.

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Driven by key law enforcement and commercial applications, research on face recognition from video sources has intensified in recent years. The ensuing results have demonstrated that videos possess unique properties that allow both humans and automated systems to perform recognition accurately in difficult viewing conditions. However, significant research challenges remain as most video-based applications do not allow for controlled recordings. In this survey, we categorize the research in this area and present a broad and deep review of recently proposed methods for overcoming the difficultie

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42

Tyagi, Ranbeer, Geetam Singh Tomar, and Namkyun Baik. "A Survey of Unconstrained Face Recognition Algorithm and Its Applications." International Journal of Security and Its Applications 10, no. 12 (2016): 369–76. http://dx.doi.org/10.14257/ijsia.2016.10.12.30.

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43

Prabhu, U., Jingu Heo, and M. Savvides. "Unconstrained Pose-Invariant Face Recognition Using 3D Generic Elastic Models." IEEE Transactions on Pattern Analysis and Machine Intelligence 33, no. 10 (2011): 1952–61. http://dx.doi.org/10.1109/tpami.2011.123.

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44

Santiago-Raḿırez, Everardo, J. ́. A. Gonźalez-Fraga, and Sixto Ĺazaro-Mart́ınez. "Face recognition and tracking using unconstrained non-linear correlation filters." Procedia Engineering 35 (2012): 192–201. http://dx.doi.org/10.1016/j.proeng.2012.04.180.

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45

Chen, Guanhao, Yanqing Shao, Chaowei Tang, Zhuoyi Jin, and Jinkun Zhang. "Deep transformation learning for face recognition in the unconstrained scene." Machine Vision and Applications 29, no. 3 (2018): 513–23. http://dx.doi.org/10.1007/s00138-018-0907-1.

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46

Pinto, Nicolas, and David D. Cox. "High-throughput-derived biologically-inspired features for unconstrained face recognition." Image and Vision Computing 30, no. 3 (2012): 159–68. http://dx.doi.org/10.1016/j.imavis.2011.12.009.

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47

Gupta, Sandeep Kumar, Seid Hassen Yesuf, and Neeta Nain. "Real-Time Gender Recognition for Juvenile and Adult Faces." Computational Intelligence and Neuroscience 2022 (March 17, 2022): 1–15. http://dx.doi.org/10.1155/2022/1503188.

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Facial gender recognition is a crucial research topic due to its comprehensive use cases, including a demographic gender survey, visitor profile identification, targeted advertisement, access control, security, and surveillance from CCTV. For these real-time applications, the face of a person can be oriented to any angle from the camera axis, and the person can be of any age group, including juveniles. A child’s face consists of immature craniofacial feature points in texture and edge compared to an adult face, making it very hard to recognize gender using the child’s face. Real-word faces cap

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48

Shyam, Radhey, and Yogendra Narain Singh. "Multialgorithmic Frameworks for Human Face Recognition." Journal of Electrical and Computer Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/4645971.

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This paper presents a critical evaluation of multialgorithmic face recognition systems for human authentication in unconstrained environment. We propose different frameworks of multialgorithmic face recognition system combining holistic and texture methods. Our aim is to combine the uncorrelated methods of the face recognition that supplement each other and to produce a comprehensive representation of the biometric cue to achieve optimum recognition performance. The multialgorithmic frameworks are designed to combine different face recognition methods such as (i) Eigenfaces and local binary pa

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Wu, Qin, and Guodong Guo. "Gender Recognition from Unconstrained and Articulated Human Body." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/513240.

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Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-b

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Adi Sufian Chan, Aifian, Mohammad Faiz Liew Abdullah, Saizalmursidi Md Mustam, Farhana Ahmad Po'ad, and Ariffuddin Joret. "Face Recognition System using Dual Network." Journal of Advanced Research in Applied Sciences and Engineering Technology 62, no. 4 (2024): 1–12. https://doi.org/10.37934/araset.62.4.112.

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Face recognition systems are the most widely used biometric technologies due to three reasons: collectability, distinctness and the rise of artificial intelligence. Deep learning-based face recognition systems are robust but require a balance between performance and speed. Additionally, recognition accuracy can be severely impacted by unconstrained environments. This paper proposes a dual network face recognition system for the two main tasks, which are face detection and face recognition. E-Face was introduced for face detection, while FRM is presented as a solution for face recognition. The

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