CDBN: Crow Deep Belief Network Based on Scattering and AAM Features for Age Estimation

Abstract

Automatic age estimation from the face images is a growing research interest nowadays. Various literature works have contributed towards the age detection scheme, besides only a few have resulted in providing good performance. This is due to the influence of the external factors, such as environment, lifestyle, and various expressions present in the face image. This paper proposes a deep belief network with the crow optimization algorithm for the age detection purpose. The proposed Crow Deep Belief Network (CDBN) finds the age of the person in the image through the initial training with the face features. The features for the training of the proposed CDBN are provided by the scattering transform and the Active Appearance Model (AAM). The training of the CDBN with the features provides the optimal weights used for the age detection. The experimentation of the proposed CDBN is done by four standard databases, namely the IMDB database, the Adience database, the AFAD database, and the FG-NET database based on the metrics, such as Mean Absolute Error (MAE), Accuracy of error of one age category (AEO) and Accuracy of an Exact Match (AEM). Among them, the proposed model has the minimum MAE with a value of 2.186 for FG-NET database, and maximum AEO and AEM with the values of 0.972, and 0.971, respectively for IMDB database.