Face detection using Min-Max features enhanced with Locally Linear Embedding

• Published in: TEM Journal
• Main Author: Hidayat R.; Jaafar F.N.; Yassin I.M.; Zabidi A.; Zaman F.H.K.; Rizman Z.I.
• Format: Article
• Language: English
• Published: UIKTEN - Association for Information Communication Technology Education and Science 2018
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052222481&doi=10.18421%2fTEM73-27&partnerID=40&md5=524eb1aa2d64f4140dd9ac1c93902f5d

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. © 2018 Rahmat Hidayat et al.