Age-Based Human Face Image Retrieval Using Zernike Moments

Aging is the process of appearing some variations on human face which facilitate the task of retrieving the facial image of the same individual at different ages. This paper proposes a new image retrieval which takes facial image and the age of individual as the queries and retrieves the face image or the most similar face image of that person in the selected age. The proposed method utilizes the Zernike Moments (ZM) as a feature extraction approach and Multi-Layer Perceptron (MLP) neural network as a learning method. In this approach, we use aging attributes and orthogonal moments features to imply a new application in the field of face image retrieval. Evaluation of the proposed method on FG-NET and MORPH datasets indicates the superiority of the proposed method over several state-of-the-art methods.

Aging is the process of appearing some variations on human face which facilitate the task of retrieving the facial image of the same individual at different ages. This paper proposes a new image retrieval which takes facial image and the age of individual as the queries and retrieves the face image or the most similar face image of that person in the selected age. The proposed method utilizes the Zernike Moments (ZM) as a feature extraction approach and Multi-Layer Perceptron (MLP) neural network as a learning method. In this approach, we use aging attributes and orthogonal moments features to imply a new application in the field of face image retrieval. Evaluation of the proposed method on FG-NET and MORPH datasets indicates the superiority of the proposed method over several state-of-the-art methods.