Automatic extraction of head and face boundaries and facial features
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
Information theory for Gabor feature selection for face recognition
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EURASIP Journal on Applied Signal Processing
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This paper presents a systematic analysis of Gabor filter banks for detection of facial landmarks (pupils and philtrum). Sensitivity is assessed using the A' statistic, a non-parametric estimate of sensitivity independent of bias commonly used in the psychophysical literature. We find that current Gabor filter bank systems are overly complex. Performance can be greatly improved by reducing the number of frequency and orientation components in these systems. With a single frequency band, we obtained performances significantly better than those achievable with current systems that use multiple frequency bands. Best performance for pupil detection was obtained with filter banks peaking at 4 iris widths per cycle and 8 orientations. Best performance for philtrum location was achieved with filter banks with 5.5 iris widths per circle and 8 orientations.Gabor filter banks [4] are reasonable models of visual processing in primary visual cortex [5, 3] and are one of the most successful approaches for processing images of the human face [6, 1, 2, 8]. The success of the approach parallels the success of bandpass filter banks, which approximate signal processing in the cochlea, in speech recognition problems. While the optimal filter bank characteristics have been extensively studied in the speech recognition literature, little work has been done to systematically explore which frequency and orientation bands are optimal for face processing applications. The goal of this paper is to start addressing this gap in the literature. To evaluate performance of the different filter bank approaches, we use a standard recognition engine (nearest neighbor) and measure sensitivity using the A' statistic. This is a non-parametric measure of sensitivity commonly used in the psychophisical literature.