Abstract
In the recent years, we have witnessed the rapid development of face recognition, though it is still plagued by variations such as facial expressions, pose, and occlusion. In contrast to the face, the ear has a stable 3D structure and is nearly unaffected by aging and expression changes. Both the face and ear can be captured from a distance and in a nonintrusive manner, which makes them applicable to a wider range of application domains. Together with their physiological structure and location, the ear can readily serve as supplement to the face for biometric recognition. It has been a trend to combine the face and ear to develop nonintrusive multimodal recognition for improved accuracy, robustness, and security. However, when either the face or the ear suffers from data degeneration, if the fusion rule is fixed or with inferior flexibility, a multimodal system may perform worse than the unimodal system using only the modality with better quality sample. The biometric quality-based adaptive fusion is an avenue to address this issue. In this paper, we present an overview of the literature about multimodal biometrics using the face and ear. All the approaches are classified into categories according to their fusion levels. In the end, we pay particular attention to an adaptive multimodal identification system, which adopts a general biometric quality assessment (BQA) method and dynamically integrates the face and ear via sparse representation. Apart from a refinement of the BQA and fusion weights selection, we extend the experiments for a more thorough evaluation by using more datasets and more types of image degeneration.
Highlights
Face recognition (FR) has been intensively studied and received significant progress in the recent decade
All the approaches are classified according to their fusion methodologies
Multimodal biometric systems are believed to improve the recognition accuracy and robustness by integrating evidence presented by multiple biometric modalities
Summary
Face recognition (FR) has been intensively studied and received significant progress in the recent decade. Feature fusion schemes are expected to pertain most of the discriminative information from multiple biometric sources while containing less redundant data; thereby, they are expected to be the best way to improve multibiometric performance. After transformed into a common domain, the match scores can be combined by using some simple rules such as Sum, Product, and Max or Min rules, or we concatenate all scores to form a score vector, and this vector is classified using Fisher’s discriminant analysis, support vector machine (SVM), Bayesian classifier, neural network, and decision tree Fusion approaches at this level are most commonly used in the biometric literature primarily due to the ease of accessing and processing match scores. In contrast to feature-level fusion, fusion at the score level is applicable to all kinds of multibiometric systems, while the information contained in matching scores is much richer than ranks and decisions. Feature-level fusion is generally believed to have potential on exploiting the most discriminative information contained in the raw data so as to pull the multibiometric performance to a higher level
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