Abstract
Accurate recognition of person identity is a critical task in civil society for various application and different needs. There are different well-established biometric modalities that can be used for recognition purposes such as face, voice, fingerprint, iris, etc. Recently, face images have been widely used for person recognition, since the human face is the most natural and user-friendly recognition method. However, in real-life applications, some factors may degrade the recognition performance, such as partial face occlusion, poses, illumination conditions, facial expressions, etc. In this paper, we propose two dynamic feature subset selection (DFSS) methods to achieve better recognition for occluded faces. The proposed DFSS methods are based on resilient algorithms attempting to minimize the negative influence of confusing and low-quality features extracted from occluded areas by either exclusion or weight reduction. Principal Component Analysis and Gabor filtering based approaches are initially used for face feature extraction, then the proposed DFSS methods are dynamically enforced. This is leading to more effective feature representation and an improved recognition performance. To validate their effectiveness, multiple experiments are conducted and the performance of different methods is compared. The experimental work is carried out using AR database and Extended Yale Face Database B. The obtained results of face identification and verification show that both proposed DFSS methods outperform the standard (static) use of the whole number of features and the equal feature weights.
Highlights
Biometrics has long been known as a robust approach for person recognition, using various physiological/behavioral traits such as face, voice, fingerprint, iris, gait, etc. [1]
Developing more reliable face biometric systems for occluded face identification/verification have been increasingly becoming an urgent need for nowadays global face-mask occlusion accompanying COVID19 epidemic
We proposed two dynamic feature subset selection (DFSS) methods to improve face recognition performance on occluded face images
Summary
Biometrics has long been known as a robust approach for person recognition, using various physiological/behavioral traits such as face, voice, fingerprint, iris, gait, etc. [1]. The majority of existing real-world biometric systems are based on unimodal biometric recognition, which makes use of a single biometric modality and needs to be accurately enrolled in database for training the algorithm, needs to be sufficiently acceptable and usable in recaptured probe or test samples for achieving successful. Such biometric systems may still suffer from several limitations, especially with unexpected or uncontrolled test or query data used to probe the biometric system, such as occlusions, variations, noise, and low quality [2,3]. The wide spread of commonly masked faces everywhere has become a serious concern to be considered and a real challenge to be confronted by face biometric systems for person identification or verification (authentication). Several security-related face recognition issues have been escalated in many regions after dozens of crimes committed by criminals taking advantage of COVID-19 face-covering rules
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