Face recognition has a wide range of applications such as personal identification and authentication, criminal identification, security and surveillance, image and film processing, and human-computer interaction. Although many methods exist, this paper proposes recent face recognition using a dynamic programming algorithm for image recognition and classification. Method based on a new mapping network called wavelet-network namely Wavenet transform (WN). WN was employed to make approximation to the images before passing through the discrete wavelet transform decomposition to extract the image descriptive features. These features are used in the proposed image identification algorithm for enhancing the accuracy of recognition at pixel level and to minimize the additive cost function.

The proposed hybrid transform is based on the combination of the Wavenet (WN) and the Inverse Discrete Wavelet Transform (IDWT) followed by a Neural Network (NN) to be considered as feature extractor for the given image. In this paper the neural network (NN) classifier is combined with the wavelet transform. A reference set of 100 images are used and collected from different data images. This method gave an excellent and a successful identification rate of 99%.  Gaussian noise was added for further testing; the proposed algorithm for the same collected images and identification rate of 95% was achieved with level of up to 0.10.

The algorithm was implemented using MATLAB programming languages version 7.