Face recognition using neural networks on field programmable gate array

Biometric is a science of digital technology which is used to identify people based on unique physical or biological characteristics. There are several biometric technologies such as fingerprint, face, iris and speech recognition. The feature extraction techniques play important role for biometric recognition system design.Recently, the Field Programmable Gate Arrays (FPGAs) have been commonly used in several applications such as digital signal processing, biometric recognition, medical imaging aerospace and defense systems, computer vision. Basically, FPGAs are the programmable logic devices. Each function of logic block can be organized by user. FPGAs are preffered in a variety of applications.In this thesis, a face recognition system which is implemented on FPGA has been introduced. The principle component analysis (PCA) has been used for feature extraction and recognition has been accomplished by artificial neural network (ANN).Since the training of the artificial neural network is a long process using only one processor on FPGA, a hierarchical classification with multiple processor approach has been followed. Thus, 47.2% system speedup has been obtained for a recognition rate of 93.9%.