Face Recognition Using Different Local Features with Different Distance Techniques

Abstract

A face recognition system using different local features with different distance measures is proposed in this paper. Proposed method is fast and gives accurate detection. Feature vector is based on Eigen values, Eigen vectors, and diagonal vectors of sub images. Images are partitioned into sub images to detect local features. Sub partitions are rearranged into vertically and horizontally matrices. Eigen values, Eigenvector and diagonal vectors are computed for these matrices. Global feature vector is generated for face recognition. Experiments are performed on benchmark face YALE database. Results indicate that theproposed method gives better recognition performance in terms of average recognized rate and retrieval time compared to the existing methods.

Perbandingan Rapid Centroid Estimation (RCE) — K Nearest Neighbor (K-NN) Dengan K Means — K Nearest Neighbor (K-NN)

“…Lamda ( ) merupakan parameter penentu dan bernilai bilangan positif dari 1 sampai dengan tak terhingga (∞), jika nilai λ = 1 maka ruang jarak minkowsky sama dengan manhattan [2], dan jika λ = 2 ruang jaraknya sama dengan euclidean [14], dan jika λ= ∞ sama dengan ruang jarak chebyshev [25].…”