Development of a face recognition system using hybrid Genetic-principal component analysis
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Date
2021-12
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1st International Conference on Electrical, Electronic, Computer Engineering & Allied Multidisciplinary Field
Abstract
Humans have been using physical attributes such as face, voice gait and fingerprints to recognize each other for ages. With the recent technological advancement, face recognition is a branch of biometrics system which has received considerable interest because of its ease in collecting, analysing and recognising face images. It is a system which compares an unknown image against the trained images in a database in order to identify the image. It has a number of applications such as Automatic Teller Machine (ATM), credit card, physical access control, National Identity card and correctional facilities. It has been found to be one of the ways of controlling and reducing crime rate. The development and evaluation of the performance of a face recognition system using hybrid Genetic- principal component Analysis technique is presented. The system consists of three major subsystems. Initial preprocessing procedures are applied on the input face images selected from the ORL Database. Consequently, face features are extracted from the processed images by principal component analysis and finally face identification is carried out using Genetic algorithm. Image resolutions of 50 x 50, 70 x 70, 100 x 100 and 140 x 140 are used in training and testing the system. The identification rates obtained were 100%, 96.36%, 93.63% and 90.90% for 50 x 50, 70 x 70, 100 x 100 and 140 x 140 respectively. This experimental result revealed that the lower the resolution of the cropped images, the higher the number of the correctly identified face images. The reason is attributed to the fact that there is variation in the features considered for recognition for each resolution. Hence, this technique has been proved to be more robust and suitable for low resolution.
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Keywords
Face recognition, Genetic Algorithm, Principal component Analysis, Identification rate