Multi-Biometric Fusion By Neural Networks

As a novel personal identification technology using certain physiological or behavioral traits associated with the person, Biometrics has always been an attractive topic of research. Biometric systems make use of fingerprints, hand geometry, iris, retina, face, hand vein, facial thermograms, signature or voiceprint to verify a person's identity. They have an edge over traditional security methods in that they cannot be easily stolen or shared. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones and ATMs.Neural networks approach has been regarded as an interesting and powerful tool for biometrics identification. Since 1980s, more and more experts from some famous universities such as MIT and Harvard universities have been doing research on the theories and the applications of neural networks. Many important achievements have been reported on some first class international journals such as Science and Nature.This thesis studies the problem of multi-modal biometric fusion by using neural networks. The main contributions of this thesis are as follows:1. Using neural networks to study holistic and partial based face recognition. It combines Neural Networks, Principal Component Analysis (PCA), Non-negative Matrix Factorization with Sparseness constraints (NMFs), Radial Basis Function (RBF), Fisher's Linear Discriminant (LDA or FLD), to investigate face recognition for face images with large variations in lighting, pose, facial expression and partial occlusion noise or partially damaged facial images. A novel partial facial features extracting method by combining NMFs with FLD (FNMFs) is proposed, and the RBF classifier is then applied to classify the facial images with large variations. A comparative analysis engages PCA-FLD (FPCA) method and FNMFs method for both parts-based and holistic-based face recognition. The comparative experiments show that FNMFs has better performance than FPCA-based method for face recognition.2. Using subspace associative memory with continuous attractors to study face recognition. The traditional associative memories with fixed-point attractors and associative memories with continuous attractors are studied. A subspace associative memory with continuous attractors is proposed. It is applied to recognize partially damaged or occluded facial images. The theoretical expressions are plotted, and the comparative experiments are carried out. It shows that partial-feature-based subspace associative memory outperforms holistic-feature-based subspace method significantly in recognizing partially damaged faces, and the subspace associative memory can learn and store some continuous attractors for completion partially damaged face images.3. Some algorithms are proposed on continuous and discrete binary particle swarm optimization (PSO). In these algorithms, both maximum and minimum velocities are controlled to improve the abilities of the convergence by applying the theory of negative selection in Artificial Immune System (AIS). Two multilayer perceptron networks are successfully trained by the PSO with minimum and maximum velocity constraints in order to overcome premature convergence and alleviate the influence of dimensionality increasing.4. A novel binary PSO algorithm based on Adaptive Neuro-Fuzzy Inference System and Artificial Immune System for face recognition is proposed to select the fusion rules by minimizing the Bayesian error cost. Such fusion rules are applied to face recognition as well as fusion face and fingerprint. Experimental results show that the proposed fusion algorithm outperforms individual algorithms that based on PCA or NMFs.