Deep face recognition: A survey
TL;DR: A comprehensive review of the recent developments on deep face recognition can be found in this paper, covering broad topics on algorithm designs, databases, protocols, and application scenes, as well as the technical challenges and several promising directions.
About: This article is published in Neurocomputing.The article was published on 2021-03-14 and is currently open access. It has received 353 citations till now. The article focuses on the topics: Deep learning & Feature extraction.
Citations
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TL;DR: Li et al. as mentioned in this paper proposed an identity and pose disentangled facial expression recognition (IPD-FER) model to learn more discriminative feature representation, which achieved state-of-the-art recognition performance.
Abstract: Facial expression recognition (FER) is a challenging problem because the expression component is always entangled with other irrelevant factors, such as identity and head pose. In this work, we propose an identity and pose disentangled facial expression recognition (IPD-FER) model to learn more discriminative feature representation. We regard the holistic facial representation as the combination of identity, pose and expression. These three components are encoded with different encoders. For identity encoder, a well pre-trained face recognition model is utilized and fixed during training, which alleviates the restriction on specific expression training data in previous works and makes the disentanglement practicable on in-the-wild datasets. At the same time, the pose and expression encoder are optimized with corresponding labels. Combining identity and pose feature, a neutral face of input individual should be generated by the decoder. When expression feature is added, the input image should be reconstructed. By comparing the difference between synthesized neutral and expressional images of the same individual, the expression component is further disentangled from identity and pose. Experimental results verify the effectiveness of our method on both lab-controlled and in-the-wild databases and we achieve state-of-the-art recognition performance.
5 citations
01 Jan 2020
TL;DR: In this chapter, comparative analysis for important deep learning models towards face recognition is presented and experimental results are presented considering their comparative analysis against benchmark datasets.
Abstract: Deep learning networks have established themselves as a promising model for face recognition. Their success is attributed towards multiple processing layers in order to learn data representations with several feature extraction levels. Convolutional neural networks have been present as the deep learning tool in almost all face recognition systems. The significant breakthrough made by DeepIDs, DeepFace, Face++, FaceNet, and Baidu has changed the entire investigation scope. The deep face recognition techniques leverage hierarchical architecture in order to learn discriminative face representation. It has improved the system’s performance appreciably which has led to the growth of several successful applications. In this chapter, comparative analysis for important deep learning models towards face recognition is presented. A multi-stage strategy is used in order to prepare the experimental datasets. These datasets are developed from readily available web knowledge sources. The datasets are used for model training as well as evaluation. The experimental results are presented considering their comparative analysis against benchmark datasets. Several deep face recognition issues and other open questions in deep face recognition are also discussed with directions towards future research.
5 citations
12 Oct 2019
TL;DR: This work proposes a novel deep architecture called Deep Adaptive Label Distribution Learning (DALDL), which uses discrete label distribution of possible ratings rather than single label to supervise the learning process of facial attractiveness prediction, and update the label distribution automatically during training process.
Abstract: One of the biggest challenges in the problem of facial attractiveness prediction is the lack of reliable labeled training data. It is very hard to apply a well defined concept to describe the attractiveness of a face. In fact, facial attractiveness prediction is a label ambiguity problem. In order to solve the problem, we propose a novel deep architecture called Deep Adaptive Label Distribution Learning (DALDL). Different from previous works, we use discrete label distribution of possible ratings rather than single label to supervise the learning process of facial attractiveness prediction, and update the label distribution automatically during training process. Our approach provides a better description for facial attractiveness, and experiments have shown that DALDL achieves better or comparable results than the state-of-the-art methods.
4 citations
TL;DR: There is a need to design algorithms which are robust to the visual changes caused by facial tattoos and paintings, especially for images where a large area of the face is covered with tattoos or paintings.
Abstract: In the past years, face recognition technologies have shown impressive recognition performance, mainly due to recent developments in deep convolutional neural networks. Notwithstanding those improvements, several challenges which affect the performance of face recognition systems remain. In this work, we investigate the impact that facial tattoos and paintings have on current face recognition systems. To this end, we first collected an appropriate database containing image-pairs of individuals with and without facial tattoos or paintings. The assembled database was used to evaluate how facial tattoos and paintings affect the detection, quality estimation, as well as the feature extraction and comparison modules of a face recognition system. The impact on these modules was evaluated using state-of-the-art open-source and commercial systems. The obtained results show that facial tattoos and paintings affect all the tested modules, especially for images where a large area of the face is covered with tattoos or paintings. Our work is an initial case-study and indicates a need to design algorithms which are robust to the visual changes caused by facial tattoos and paintings.
4 citations
25 Mar 2019
TL;DR: This research presents the consequences of recognition rates on the AT&T face database, using the hybrid methods of ZCA feature extraction and ANN in order to reduce computational power usage.
Abstract: Nowadays, there are deep learning-based face recognition systems with reliable accuracy rates. But these systems need high computational powers because they should do feature extraction. This research presents the consequences of recognition rates on the AT&T face database, using the hybrid methods of ZCA feature extraction and ANN in order to reduce computational power usage.
4 citations
References
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TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
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TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
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