Face Recognition and Gender Classification Using Orthogonal Nearest Neighbour Feature Line Embedding

Abstract

In this paper, a novel manifold learning algorithm for face recognition and gender classificationorthogonal nearest neighbour feature line embedding (ONNFLE) -is proposed. Three of the drawbacks of the nearest feature space embedding (NFSE) method are solved: the extrapolation/interpolation error, high computational load and non-orthogonal eigenvector problems. The extrapolation error occurs if the distance from a specified point to one line is small when that line passes through two farther points. The scatter matrix generated by the invalid discriminant vectors does not efficiently preserve the locally topological structureincorrect selection reduces recognition. To remedy this, the nearest neighbour (NN) selection strategy was used in the proposed method. In addition, the high computational load was reduced using a selection strategy. The last problem involved solving the nonorthogonal eigenvectors found with the NFSE algorithm. The proposed algorithm generated orthogonal bases possessing more discriminating power. Experiments were conducted to demonstrate the effectiveness of the proposed algorithm.

Dimensionality reduction method based on multiple feature-space collaborative discriminative projection for rotor fault diagnosis

“…Imani et al used a combination of median-mean line (MML) and feature line (FL) metrics to eliminate the negative impact of outliers on the class mean and improve the efficiency of the algorithm dimensional reduction [25]. In addition, scholars have also proposed extended versions of feature spacebased algorithms, such as orthogonal nearest neighbor feature line embedding (ONNFLE) [26], fuzzy kernel NFLE (FKN-FLE) [27], multiple kernel feature line embedding (MKFLE) [28], space-to-space (S2S)-based metric learning (FSDML) [29], support vector machine (SVMFLE)-based feature line embedding [30], and other methods.…”