Using face detection for browsing personal slow video in a small terminal and worn camera context

A technique based on face detection is proposed which assists this user in finding, in such "slow video" collections, time intervals corresponding to meetings with people, using a worn camera to serve as a visual memory for its user.

Abstract

This paper addresses an original issue at the intersection of image sequence analysis, content-based retrieval and wearable computing. The emerging combination of small-size personal digital imaging and communication device terminals (enhanced mobile phones etc.) is enabling the build up of large personal image collections, which induce content-based retrieval issues particular to this context. We consider here the case of a worn camera, automatically and regularly taking pictures, so as to serve as a visual memory for its user. We propose a technique based on face detection which assists this user in finding, in such "slow video" collections, time intervals corresponding to meetings with people. To this purpose, face detection is first run independently on successive images. Its noisy output is then considered the observation sequence in a regularization process conducted with a Viterbi estimation algorithm. The result can be usefully overlaid on a PDA calendar manager. Experiments validate the technique on real data.