Cascade AdaBoost Classifiers with Stage Features Optimization for Cellular Phone Embedded Face Detection System

Abstract

In this paper, we propose a novel feature optimization method to build a cascade Adaboost face detector for real-time applications on cellular phone, such as teleconferencing, user interfaces, and security access control. AdaBoost algorithm selects a set of features and combines them into a final strong classifier. However, conventional AdaBoost is a sequential forward search procedure using the greedy selection strategy, redundancy cannot be avoided. On the other hand, design of embedded systems must find a good trade-off between performances and code size due to the limited amount of resource available in a mobile phone. To address this issue, we proposed a novel Genetic Algorithm post optimization procedure for a given boosted classifier, which leads to shorter final classifiers and a speedup of classification. This GA-optimization algorithm is very suitable for building application of embed and resource-limit device. Experimental results show that our cellular phone embedded face detection system based on this technique can accurately and fast locate face with less computational and memory cost. It runs at 275ms per image of size 384×286 pixels with high detection rates on a SANYO cellular phone with ARM926EJ-S processor that lacks floating-point hardware.