- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Face recognition is one of the most challenging aspect in the field of image analysis. Face recognition has been a topic of active research since the 1980’s, proposing solutions to several practical problems. Face recognition is probably the biometric method that is used to identify people mainly from their faces. However, the recognition process used by the human brain for identifying faces is very challenging. In this paper, a Genetic Algorithm (GA) based approach is proposed for face recognition. The proposed algorithm recognizes an unknown image by comparing it with the known training images stored in the database and gives information regarding the person recognized. The proposed algorithm is then compared with other known face recognition algorithms viz: Principal Component Analysis(PCA) and Linear Discriminate Analysis (LDA) algorithms. It has been observed that the recognition rate of the proposed algorithm is better.
6. Conclusion and Future Scope
Face recognition is one of the challenging aspect in the field of image analysis and computer vision. The focus towards the face recognition has been increased in the last few years due to its enormous applications in di erent domains. The research conducted in this field for the past four decades leads to encouraging results but still we are unable to find the face recognition technique which is able to perform e ciently in the various situations commonly encountered in daily life. The algorithms related to face recognition technique are thoroughly studied taking a number of test images and varying the conditions and variables. Proposed Genetic algorithm based method is applied on three di erent benchmarked databases: ORL (Olivetti Research Laboratory), UMIST and Indbase. The ultimate objective of the research work is to improve the recognition rate. The proposed method gives better recognition rate as com-pared to existing PCA and LDA methods. It has been observed that the proposed Genetic algorithm based method has achieved the 98.57 % face recognition rate with ORL database, 100 % recognition rate with UMIST database and 98.33 % recognition rate with Indbase database which is far better than the existing techniques PCA and LDA. The proposed work can further be improved using other optimization algorithms and can also be applied on other benchmarked databases.