A reliable descriptor for face objects in visual content

We present a descriptor for human face objects in visual content. The descriptor enables similarity-based retrieval using a face image as the query. The descriptor for a set of face objects consists of three components: a face subspace that is computed using principal component analysis, a discriminant matrix that classifies the set of faces, and a collection of face vectors with each vector corresponding to a particular face object in the set. Each face vector is computed by projecting the face image onto the face subspace and then onto classification space using the discriminant matrix. In the classification space, faces of a person are distinctly clustered, and hence it becomes simpler to classify a novel image when projected onto that space. Similarity is measured in terms of the Euclidean distance measure. We demonstrate the efficacy of the descriptor for similarity-based retrieval using MPEG-7 test content. We also discuss how the descriptor satisfies some key requirements of MPEG-7.