Facial signatures for fast individual retrieval from video dataset

The topic of retrieving videos containing a desired person by using the content of faces without any help of textual information has many interesting applications like video surveillance, social network, video mining, etc. However, face-by-face matching leads to an unacceptable response time for a video dataset with a large number of detected faces and may also reduce the accuracy of searching. Therefore, in this paper we propose a scheme to generate facial signatures for fast retrieving videos containing the same person with a query. First, we summarize each video as a set of person-oriented individuals based on detected faces, which are represented as high dimensional vectors in a feature space. Then, each person with a collection of high dimensional vectors is projected to a compact and reduced dimensionality representation that is called facial signature for this person. The projection is realized by constructing a matcher using linear discriminant analysis with maximum correntropy criterion optimization. In this research, two kinds of signatures are provided, which are called 1D facial signature and 2D facial signature. The proposed searching scheme can support two types of queries: face image and video clip. Evaluations on a large dataset of videos show reliable measurement of similarities by using facial signature to represent each person generated from videos and also demonstrate that the proposed searching scheme has the potential to substantially reduce the response time and slightly increase the mean average precision of retrieval.