An MLP-based face authentication technique robust to orientation

The need for stricter security, availability of sophisticated algorithms and improved hardware at cheaper price, are the driving forces for increasing popularity of biometric authentication. Machine authentication from face images or fingerprints at the entrance of a building or at bank-teller is getting more and more common. The popularity of using face image features for authentication is due to its ease of use. But its success depends on proper orientation and illumination. Facial features change with the angle of orientation, and even a genuine person would be rejected by the machine due to improper orientation of the face towards the camera. In this work, we proposed a multi-layer perceptron based face identification technique which is robust to orientation of the face image under similar illumination condition. Training data of facial features against orientation angle features is used to train a Multi-Layer Perceptron (MLP). It is then used for interpolation of facial features at different angles. Experiments were conducted with two types of face image identifying features, using PCA and ICA. A good interpolation property could be obtained by the trained MLP, and a zero equal error rate (EER) could be achieved.