A new fuzzy and Gaussian distribution induced two-directional inverse FDA for feature extraction and face recognition. (20th May 2022) - Dey, Aniruddha; Chowdhury, Shiladitya; Sing, Jamuna Kanta
In the area of face recognition research, the high dimensionality of the data is indeed a crucial problem. This paper proposes a new fuzzy and Gaussian distribution induced two-directional inverse Fisher's discriminant analysis (FGD-2DIFDA) which computes the fuzzy and Gaussian distribution membership values and thereby combined the values with the training samples to obtain the class-wise mean and the global mean. These fuzzy and Gaussian function induced membership values are incorporated in inter- and intra-class scatter matrices for diminishing the effect of uncertainty formed by variation of light facial expression and pose. Finally, the eigenvalue problems of the inverse Fisher's criteria are solved to finds the optimal inverse projection vectors. The uncertainty associated in the image region is efficiently managed by the FGD-2DIFDA method than fuzzy generalised 2DFLD (FG-2DFLD) method as two membership values are employed in the former case while the later one uses only one membership value to handle such situation. The FGD-2DIFDA method has been evaluated on the AT&T (formally known as ORL), UMIST and FERET face databases using support vector machine (SVM) as classifier. Simulation results demonstrate that the proposed FGD-2DIFDA method can obtain higher recognition rate than some state-of-the-art face recognition methods.