2026· International journal of research and scientific innovation· Vol 13, pp. 3763-3775· 0 citations
TL;DR
A comparative analysis of existing studies is presented to highlight the evolution of deep learning techniques and their effectiveness in improving recognition accuracy and computational efficiency and emerging research directions are outlined to provide insights for future research.
Abstract
Face recognition has become one of the most prominent biometric technologies due to its extensive applications in surveillance, access control, authentication, human-computer interaction, and intelligent security systems. Recent advances in deep learning, particularly Convolutional Neural Networks (CNNs), have significantly improved the accuracy and robustness of face detection, feature extraction, and face recognition under challenging real-world conditions. This paper presents a comprehensive review of deep learning-based techniques employed across the complete face recognition pipeline. A comparative analysis of existing studies is presented to highlight the evolution of deep learning techniques and their effectiveness in improving recognition accuracy and computational efficiency. The review also discusses widely used benchmark datasets, performance evaluation metrics, and the major challenges encountered in unconstrained environments, such as pose variation, illumination changes, occlusion, facial expressions, aging, and low-resolution imagery. Finally, emerging research directions, including lightweight deep learning models, attention mechanisms, Vision Transformers, self-supervised learning, explainable artificial intelligence, and real-time video-based face recognition, are outlined to provide insights for future research. This review serves as a comprehensive reference for researchers and practitioners seeking a thorough understanding of recent developments and future trends in deep learning-based face recognition.
Face recognition is a cornerstone in computer vision with its applications covering emotion analysis, healthcare and human computer interaction. This paper gives a complete explanation and classification of face recognition models by categorizing into Machine learning (ML), Deep Learning (DL) and hybrid techniques. Traditional methods rely on manual features and statistical evaluations that are relevant, whereas DL methods used convolutional and transformer-based techniques for classification and feature extraction. The integration of both techniques (hybrid) with its benefits performs better flexibility and more consistency for different conditions. This survey presents current innovations such as fuzzy similarity measures, self-distillation methods, segmentation improved Convolutional Neural Network (CNNs) and securely preserving quaternion networks. Applications including masked face detections, light variation enhancement and emotion-aware music recommendation is specified. The comparative analysis of cited works indicates that while DL models achieve superior recognition accuracy (typically >90% on benchmarks like FER-2013), they demand extensive data and computational resources, resulting in higher inference latency compared to hybrid models. Additionally, privacy concerns are an important challenge in multimodal biometric systems. This paper shows research gap in existing methods and demonstrates the demand for face recognition systems that are fast, protect user privacy and adjust with different environments for practical real world scenarios.
C. Prathibha, Ashwini S. Shivannavar· 2026 5th International Confe...· 0 citations
Face recognition becomes an important biometric implementation in any surveillance, access control, forensic and intelligent security system applications. Despite of technical advances in deep learning models, recognition accuracy normally affected by facial pose, illumination, occlusion, expression, aging factors and limited availability of labelled dataset. These challenges reduce the robustness of face recognition models in real world environments [1][2][3]. Recent researches demonstrated that the super-resolution techniques based on Generative Adversarial Networks (GANs) reconstruct high-quality facial images very effectively from low-resolution input images. It supports to improve feature representation and face recognition [4][5]. Therefore, this research motivated from this advancement and proposes an Adaptive Super-Resolution Generative Adversarial Network (Adaptive SRGAN) for face recognition. It integrates adaptive learning with image super resolution to reconstruct identity preserving high resolution facial images by employing adaptive learning rate optimization, dynamic loss weighting, attention guided feature enhancement and identity preserving loss functions. However, it enhances reconstruction quality by preserving discriminative facial characteristics [6]. The proposed model is expected to achieve higher Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), recognition accuracy, precision, recall, and F1-score while reducing false acceptance and false rejection rates. Simultaneously, Adaptive SRGAN delivers a robust and scalable solution for improving face recognition systems.
M. Kirubakaran, A. S. Aneeshkumar· International journal of com...· 0 citations
Face recognition in unconstrained environments remains a challenging problem in computer vision due to variations in pose, illumination, expression, and occlusion. This paper proposes a novel attention-enhanced ArcFace-based deep learning framework that integrates a Residual CNN backbone with Convolutional Block Attention Module (CBAM) and ArcFace loss for robust face recognition. Unlike existing approaches that rely on large-scale external pretraining datasets, the proposed framework is trained exclusively on the Labelled Faces in the Wild (LFW) dataset, demonstrating data-efficient learning. The system is evaluated on both 1:1 verification and 1:N identification protocols. Experimental results demonstrate superior performance with verification accuracy of 95.70%, identification accuracy of 89.75%, ROC-AUC of 99.16%, and True Positive Rate (TPR) of approximately 92% at a 1% False Positive Rate (FPR). The novelty lies in the synergistic integration of attention mechanisms with angular margin-based metric learning, achieving competitive performance without external pretraining. Comparative analysis with state-of-the-art methods including DeepFace, FaceNet, VGGFace, SphereFace, and baseline ArcFace validates the effectiveness of the proposed attention-guided approach for unconstrained face recognition tasks.
Samadhan S. Ghodke, Prapti D. Deshmukh· International journal of com...· 0 citations
Face recognition becomes one of the most adopted biometric technics due to its applications in intelligent surveillance, access control, border security, digital authentication, criminal investigation and human computer interaction. The development of Deep convolutional neural networks (CNNs) significantly improved accuracy of recognition even in unconstrained environments such as pose variations, illumination changes, facial expressions, occlusions and low-resolution images. Conventional CNNs mainly focus on local spatial features and so it has limited ability to preserve hierarchical association between facial components. Capsule Networks (CapsNets) overcome this by representing visual features as vector capsules with existence and geometric properties of objects. The self supervised learning of CapsNets enables feature learning from unlabelled images. This article presents a Self supervised Capsule Network (SS-CapsNet) integrates convolutional feature extraction, capsule based learning, dynamic routing and contrastive self supervised learning into a combined framework for robust face recognition. This approach simultaneously learns discriminative identity from large scale unlabelled dataset while preserves facial geometry. The SS-CapsNet provides improved robustness against pose variations, illumination changes, facial occlusions and image degradation.
K. Minney Prisilla, N. Jayashri· International journal of com...· 0 citations
Face recognition systems applied to smart surveillance settings often experience poor performance when the faces are partially occluded by a mask or other objects. Occlusions eliminate critical facial information, which makes face identification much more difficult for traditional deep learning models. To solve this issue, a hybrid deep learning model utilizing convolutional neural networks and transformer-based attention mechanism is proposed in this study for robust masked and occluded face recognition. The framework uses the ResNet50 backbone for obtaining the discriminative local facial favorable features, and the Vision Transformer module for obtaining long-range context relationships between facial regions. In addition, an Adaptive Occlusion Attention Module is introduced to Zurcrook visible facial areas and neglect the corrupted features to occlusions. Experiments were carried out on the Real-World Masked Face Dataset (RMFD) with 1205 images of 25 identities. The proposed model attained 93.46% training accuracy and Top-1 and Top-5 recognition accuracy were 51.87% and 81.33%, respectively. Additional occlusion experiments resulted in occlusion recognition accuracy of 28.63% and cross-dataset evaluation using MaskedFace-Net resulted in an average feature similarity of 0.8288. The results show that the proposed hybrid architecture enhances the recognition robustness of masked and partially obstructed facial images facing the surveillance situation.
R. R, Anbalagan E· 2026 4th International Confe...· 0 citations
Intelligent Facial Age and Gender Analytics Using Deep Learning presents an automated approach for estimating a person's age and identifying gender from facial images using deep learning techniques. The system employs a Convolutional Neural Network (CNN) to learn facial characteristics directly from images, eliminating the need for manual feature extraction. Before training, facial images undergo preprocessing steps such as face detection, resizing, and normalization to improve data quality and model performance. The trained CNN analyses facial patterns and predicts both age and gender, making the system suitable for real-time applications using a webcam or image input. The proposed framework is designed to handle images captured under different lighting conditions, poses, and facial expressions, allowing it to perform effectively in practical environments. Experimental evaluation demonstrates that the model produces reliable predictions while maintaining a simple and efficient architecture. The developed system can be applied in areas such as intelligent surveillance, human-computer interaction, demographic analysis, smart retail, and access control. Overall, the proposed framework provides an accurate, practical, and user-friendly solution for automated facial age and gender analytics using deep learning.
Raghu ram Kandula, G.Rajini· International Journal of Eng...· 0 citations