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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06 Dec 2019
TL;DR: This work aims to evaluate the use of traditional machine learning algorithms on features extracted from face images, in order to perform their recognition.
Abstract: With the increased availability of face data, machine learning becomes a very relevant tool for identify people in various applications. This work aims to evaluate the use of traditional machine learning algorithms on features extracted from face images, in order to perform their recognition. The extracted features are based on the Principal Component Analysis (PCA), that represent each face as a n-dimensional vector, where n is less than the image size. The evaluated algorithms are distance classifiers, decision trees, support vector machines and neural networks, which were evaluated in their different parametrizations. The best accuracy results were obtained by the support vector machine and artificial neural networks models (99%), with the latter presenting the highest accuracy (97%) when the same model was tested in a new database.
2 citations
21 Aug 2022
TL;DR: In this paper , a deep conditional multimodal biometric network (CMB-Net) is proposed to transform any x biometric raw input(s) yielding x reference and query instances, respectively, where 1≤x≤ N. During enrollment and query, the trained CMB model is utilized as a feature encoder.
Abstract: Multimodal biometrics has been attributed to achieving better performance compared to unimodal biometrics, despite there being some limitations on its utilization e.g. availability, deployment cost, templates management, etc. In this paper, we revolve around a generalized multimodal biometrics notion, which we coin as Conditional Multimodal Biometrics (CMB). The CMB is substantiated by a learning model which is trained with N multimodal biometrics. During enrollment and query, the trained CMB model is utilized as a feature encoder to transform any x biometric raw input(s) yielding x reference and query instances, respectively, where 1≤x≤ N. Depending on application needs, multimodal biometrics system enjoys better performance by deploying either a single biometrics, a subset, or all N modalities. As a means of realization, we consider face and periocular biometrics and propose a deep CMB network, known as CMB-Net. The CMB-Net is composed of two predictors corresponding to face and periocular with a shared-parameter convolutional backbone. Apart from classification losses for each face and periocular, a CMB loss with regularization is devised to attract periocular-face intra-subject feature embeddings and repel periocular-face inter-subject feature embeddings, whilst each face and periocular regulates one another throughout CMB-Net training. We scrutinize three CMB configurations, namely periocular conditioned by face, face conditioned by periocular and periocular-face, under the CMB regimen. Our experimental results on five periocular-face in the wild datasets demonstrate that all three CMB configurations outperform their respective baselines under both identification and verification modes.
2 citations
20 Apr 2022
TL;DR: The intuition that FV systems underperform on protected demographic groups because they are less sensitive to differences between features within those groups, as evidenced by clustered embeddings is presented.
Abstract: : Machine learning models perform face verification (FV) for a variety of highly consequential applications, such as biometric authentication, face identification, and surveillance. Many state-of-the-art FV systems suffer from unequal performance across demographic groups, which is commonly overlooked by evaluation measures that do not assess population-specific performance. Deployed systems with bias may result in serious harm against individuals or groups who experience underperformance. We explore several fairness definitions and metrics, attempting to quantify bias in Google’s FaceNet model. In addition to statistical fairness metrics, we analyze clustered face embeddings produced by the FV model. We link well-clustered embeddings (well-defined, dense clusters) for a demographic group to biased model performance against that group. We present the intuition that FV systems underperform on protected demographic groups because they are less sensitive to differences between features within those groups, as evidenced by clustered embeddings. We show how this performance discrepancy results from a combination of representation and aggregation bias. death times for White face embeddings to later than other race groups ( p < 0.05 for W × A , W × I , and W × B t -tests), indicating that White embeddings are more in the embedding space. The other race groups have peak death times that are taller and earlier than the White race group. The shorter and wider peak for the White subgroup means that there is more variety (higher variance) in H 0 death times, rather than the consistent peak around 0.8 with less variance for other race groups. This shows that there is more variance for White face distribution in the embedding space compared to other race groups, a trend that was not present in the centroid distance distribution for race groups, which showed four bell-shaped density plots. Thus, our analysis of the ( H 0 ) death times supports previous findings that the White race group is clustered differently to other race groups. We note that there is less inequality in H 0 death times for female vs. male faces, despite our p -value indicating that this discrepancy may be significant ( p < 0.05).
1 citations
Posted Content•
TL;DR: A reinforcement learning based selective pseudo-labeling method for semi-supervised domain adaptation that demonstrates superior performance to all the comparison methods and proposes a novel target margin loss for the base model training to improve its discriminability.
Abstract: Recent domain adaptation methods have demonstrated impressive improvement on unsupervised domain adaptation problems. However, in the semi-supervised domain adaptation (SSDA) setting where the target domain has a few labeled instances available, these methods can fail to improve performance. Inspired by the effectiveness of pseudo-labels in domain adaptation, we propose a reinforcement learning based selective pseudo-labeling method for semi-supervised domain adaptation. It is difficult for conventional pseudo-labeling methods to balance the correctness and representativeness of pseudo-labeled data. To address this limitation, we develop a deep Q-learning model to select both accurate and representative pseudo-labeled instances. Moreover, motivated by large margin loss's capacity on learning discriminative features with little data, we further propose a novel target margin loss for our base model training to improve its discriminability. Our proposed method is evaluated on several benchmark datasets for SSDA, and demonstrates superior performance to all the comparison methods.
1 citations
Posted Content•
TL;DR: In this paper, the authors propose a measure of recognizability of a face image that leverages a key empirical observation: an embedding of face images, implemented by a deep neural network trained using mostly recognizable identities, induces a partition of the hypersphere whereby unrecognizable identities cluster together.
Abstract: The common implementation of face recognition systems as a cascade of a detection stage and a recognition or verification stage can cause problems beyond failures of the detector. When the detector succeeds, it can detect faces that cannot be recognized, no matter how capable the recognition system. Recognizability, a latent variable, should therefore be factored into the design and implementation of face recognition systems. We propose a measure of recognizability of a face image that leverages a key empirical observation: an embedding of face images, implemented by a deep neural network trained using mostly recognizable identities, induces a partition of the hypersphere whereby unrecognizable identities cluster together. This occurs regardless of the phenomenon that causes a face to be unrecognizable, it be optical or motion blur, partial occlusion, spatial quantization, poor illumination. Therefore, we use the distance from such an "unrecognizable identity" as a measure of recognizability, and incorporate it in the design of the over-all system. We show that accounting for recognizability reduces error rate of single-image face recognition by 58% at FAR=1e-5 on the IJB-C Covariate Verification benchmark, and reduces verification error rate by 24% at FAR=1e-5 in set-based recognition on the IJB-C benchmark.
1 citations
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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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