In the past decade, automatic face recognition has received much attention by both the commercial and public sectors as an efficient and resilient recognition technique in biometrics. This thesis describes a highly accurate appearance-based algorithm for grey scale front-view face recognition - Gabor-Boosting face recognition by means of computer vision, pattern recognition, image processing, machine learning etc. The strong performance of the Gabor-boosting face recognition algorithm is highlighted by combining three key leading edge techniques - the Gabor wavelet transform, AdaBoost, Support Vector Machine (SVM). The Gabor wavelet transform is used to extract features which describe texture variations of human faces. The Adaboost algorithm is used to select most significant features which represent different individuals. The SVM constructs a classifier with high recognition accuracy. Within the AdaBoost algorithm, a novel weak learner - Potsu is designed. The Potsu weak learner is fast due to the simple perception prototype, and is accurate due to large number of training examples available. More importantly, the Potsu weak learner is the only weak learner which satisfies the requirement of AdaBoost. The Potsu weak learners also demonstrate superior performance over other weak learners, such as FLD. The Gabor-Boosting face recognition algorithm is extended into multi-class classification domain, in which a multi-class weak learner called mPotsu is developed. The experiments show that performance is improved by applying loosely controlled face recognition in the multi-class classification.