Vol.7, No.3, August 2018.                                                                                                                                                                             ISSN: 2217-8309

                                                                                                                                                                                                                eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

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


Rahmat Hidayat, Fatin Nabila Jaafar, Ihsan Mohd Yassin, Azlee Zabidi, Fadhlan Hafizhelmi Kamaru Zaman, Zairi Ismael Rizman


© 2018 Ihsan Mohd Yassin, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 7, Issue 3, Pages 678-685, ISSN 2217-8309, DOI: 10.18421/TEM73-27, August 2018.


Received: 08 July 2018
Accepted: 15 August 2018
Published: 27 August 2018




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.


Keywords –Face detection, Min-Max features (MMX), Locally Linear Embedding (LLE), Multi-Layer Perceptron (MLP), Artificial Neural Network.



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