4 research outputs found
Recent Advances in Deep Learning Techniques for Face Recognition
In recent years, researchers have proposed many deep learning (DL) methods
for various tasks, and particularly face recognition (FR) made an enormous leap
using these techniques. Deep FR systems benefit from the hierarchical
architecture of the DL methods to learn discriminative face representation.
Therefore, DL techniques significantly improve state-of-the-art performance on
FR systems and encourage diverse and efficient real-world applications. In this
paper, we present a comprehensive analysis of various FR systems that leverage
the different types of DL techniques, and for the study, we summarize 168
recent contributions from this area. We discuss the papers related to different
algorithms, architectures, loss functions, activation functions, datasets,
challenges, improvement ideas, current and future trends of DL-based FR
systems. We provide a detailed discussion of various DL methods to understand
the current state-of-the-art, and then we discuss various activation and loss
functions for the methods. Additionally, we summarize different datasets used
widely for FR tasks and discuss challenges related to illumination, expression,
pose variations, and occlusion. Finally, we discuss improvement ideas, current
and future trends of FR tasks.Comment: 32 pages and citation: M. T. H. Fuad et al., "Recent Advances in Deep
Learning Techniques for Face Recognition," in IEEE Access, vol. 9, pp.
99112-99142, 2021, doi: 10.1109/ACCESS.2021.309613
Enhanced contextual based deep learning model for niqab face detection
Human face detection is one of the most investigated areas in computer vision which plays a fundamental role as the first step for all face processing and facial analysis systems, such as face recognition, security monitoring, and facial emotion recognition. Despite the great impact of Deep Learning Convolutional neural network (DL-CNN) approaches on solving many unconstrained face detection problems in recent years, the low performance of current face detection models when detecting highly occluded faces remains a challenging problem and worth of investigation. This challenge tends to be higher when the occlusion covers most of the face which dramatically reduce the number of learned representative features that are used by Feature Extraction Network (FEN) to discriminate face parts from the background. The lack of occluded face dataset with sufficient images for heavily occluded faces is another challenge that degrades the performance. Therefore, this research addressed the issue of low performance and developed an enhanced occluded face detection model for detecting and localizing heavily occluded faces. First, a highly occluded faces dataset was developed to provide sufficient training examples incorporated with contextual-based annotation technique, to maximize the amount of facial salient features. Second, using the training half of the dataset, a deep learning-CNN Occluded Face Detection model (OFD) with an enhanced feature extraction and detection network was proposed and trained. Common deep learning techniques, namely transfer learning and data augmentation techniques were used to speed up the training process. The false-positive reduction based on max-in-out strategy was adopted to reduce the high false-positive rate. The proposed model was evaluated and benchmarked with five current face detection models on the dataset. The obtained results show that OFD achieved improved performance in terms of accuracy (average 37%), and average precision (16.6%) compared to current face detection models. The findings revealed that the proposed model outperformed current face detection models in improving the detection of highly occluded faces. Based on the findings, an improved contextual based labeling technique has been successfully developed to address the insufficient functionalities of current labeling technique.
Faculty of Engineering - School of Computing183http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:150777
Deep Learning Convolutional neural network (DL-CNN), Feature Extraction Network (FEN), Occluded Face Detection model (OFD
Face and Body Association for Video-based Face Recognition
In recent years face recognition has made extraordinary leaps, yet unconstrained video-based face identification in the wild remains an open and interesting problem. Videos, unlike still-images, offer a myriad of data for face modeling, sampling, and recognition, but, on the other hand, contain low-quality frames and motion blur. A key component in video-based face recognition is the way in which faces are associated through the video sequence before being used for recognition. In this paper, we present a video-based face recognition method taking advantage of face and body association (FBA). To track and associate subjects that appear across frames in multiple shots, we solve a data association problem using both face and body appearance. The final recovered track is then used to build a face representation for recognition. We evaluate our FBA method for video-based face recognition on a challengin