Face Detection using Min-Max Features Enhanced with Locally Linear Embedding
Face Detection using Min-Max Features Enhanced with Locally Linear Embedding

Summary/Abstract: Face detection is critical function in many embedded applications such as computer vision and security as it is widely used as preprocessor for face recognition systems. As a preprocessor, the face detection system needs to extract features from a region of interest and classify them quickly as either face or non-face. In our previous works, we have devised a feature representation method called Min-Max (MMX) feature that allows representation of a region of interest using a few data points based on the unique characteristics of vertical and horizontal summation of face regions. In this paper, we attempt to improve the classification accuracy of MMX by integrating a technique called Locally Linear Embedding (LLE), a powerful dimensionality and feature enhancement algorithm that has been used successfully in many pattern recognition tasks. To test the performance of the proposed enhancement, the LLE-treated features were compared with non-treated features using a Multi-Layer Perceptron (MLP) neural network classifier. The results indicate an increase (+1.2%) in classification accuracy of the MLPs, demonstrating the ability of LLE to enhance the representation of MMX features.

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