PSO versus AdaBoost for feature selection in multimodal biometrics

Abstract

In this paper, we present an efficient feature level fusion scheme that we apply on face and palmprint images. The features for each modality are obtained using Log Gabor transform and concatenated to form a fused feature vector. We then use Particle Swarm Optimization (PSO) scheme to reduce the dimension of this vector. Final classification is performed on the projection space of the selected features using Kernel Direct Discriminant Analysis (KDDA). Extensive experiments are carried out on a virtual multimodal biometric database of 250 users built from the face FRGC and the palmprint PolyU databases. We compare the proposed selection method with the well known Adaptive Boosting (AdaBoost) method in terms of both number of features selected and performance. Experimental results in both closed identification and verification rates show that feature fusion improves performance over match score level fusion and also that the proposed method outperforms AdaBoost in terms of reduction of the number of features and facility of implementation.

Biometric Palmprint Verification: A Dynamical System Approach

“…These systems are based on the measurable biological (anatomical and physiological) or behavioral characteristics used for the identification of an individual. Different features are used in biometric systems, such as fingerprints [20], [23], palmprint [4], [7]- [9], [12]- [14], [16], [17], [19], [21], [24], [25], [28]- [32], [35]- [42], [44], [46], [47], [52]- [63], hand geometry [26], [31], [45], [49], iris [1], [10], [51], and face [11], [18], [33], [48], [50]. Unlike conventional methods for personal identification, such as personal identification number, password, and key, these features cannot be duplicated, lost or stolen.…”

Fast learning for multibiometrics systems using genetic algorithms

“…• the EER by selecting the couple of (FAR, FRR) having the smallest absolute difference (3) and returning their average (4). By this way, we have obtained the best approaching EER with the smallest precision error.…”

Fast computation of the performance evaluation of biometric systems: Application to multibiometrics

“…The scores fusion is the main process in multimodal systems. It can be operated on the scores provided by algorithms or in the templates themselves [27]. In the first case, it is necessary to normalize the different scores as they may not evolve in the same range.…”