Content based Feature Combination Method for Face Image Retrieval using Neural Network and SVM Classifier for Face Recognition

摘要

Objectives: To propose a CBIR based face image retrieval and identification model. Method/Analysis: A model of hybrid face recognition system based on CBIR and SVM is proposed. The feature vectors from the face image database are generated by using Gabor wavelet (GW), wavelet Transformation (WT), and Principal Component Analysis (PCA). For the face image retrieval purpose, Artificial Neural Network (ANN) is adopted and its performance on the retrieval process is evaluated with PCA, WT, GW and their fusion as a feature vector. The query image is recognized from the faces returned by retrieval process by using Support Vector Machine (SVM). The experimental results indicate that the fusion of PCA, WT and GW features as a feature vector performs reasonably well for retrieval and recognition process. The experimental results also demonstrate the efficiency of the proposed approach for face recognition over existing methods when considering different performance measures such as system running time, Receiver Operating Characteristic (ROC) curve and recognition accuracy. The proposed model was evaluated with two face databases viz. Unconstrained Facial Image (UFI) and Oracle Research Laboratory (ORL) face database with a recognition accuracy of 95.42% and 98.75% respectively. Finding: The proposed system has been tested for retrieval and recognition and found with reasonable retrieval time and high recognition rate. Novelty/Improvement: The conventional model-based face recognition systems are limited in several aspects, like (1) It is usually time-consuming and expensive to collect a large amount of training facial images, (2) It is usually difficult to generalize the models when new training data or new persons are added, in which an intensive retraining process is usually required and (3) The recognition performance often scales poorly when the number of persons/ classes are very large. The proposed CBIR based retrieval and the recognition system is intended to take care of the above limitation.