In this paper, we propose a near real-time effective face recognition system for consumer applications. Since the nature of application domain requires real time result and better accuracy, it poses a serious challenge. To address this challenge, we study various classification techniques, namely, support vector machine (SVM), linear discriminant analysis (LDA) and K nearest neighbor (KNN). We observe that although KNN is as effective as SVM but KNN prohibits its usage due to high response time when data is high dimensional. To speed up KNN retrieval we propose a feature reduction technique using Principle component analysis (PCA) to facilitate near real time face recognition along with better accuracy. We apply KNN after we reduce the number of features by PCA. Hence, we test various classification approaches, namely, SVM, KNN, KNN with PCA, LDA, and LDA with PCA on a benchmark dataset and demonstrate the effectiveness of KNN with PCA over SVM and LDA.