Adaptive feature-specific imaging: a face recognition example.
We present an adaptive feature-specific imaging (AFSI) system and consider its application to a face recognition task. The proposed system makes use of previous measurements to adapt the projection basis at each step. Using sequential hypothesis testing, we compare AFSI with static-FSI (SFSI) and static or adaptive conventional imaging in terms of the number of measurements required to achieve a specified probability of misclassification (Pe). The AFSI system exhibits significant improvement compared to SFSI and conventional imaging at low signal-to-noise ratio (SNR). It is shown that for M=4 hypotheses and desired Pe=10(-2), AFSI requires 100 times fewer measurements than the adaptive conventional imager at SNR= -20 dB. We also show a trade-off, in terms of average detection time, between measurement SNR and adaptation advantage, resulting in an optimal value of integration time (equivalent to SNR) per measurement.
Baheti
Pawan K
PK
Department of Electrical and Computer Engineering, University of Arizona, Tucson, Arizona 85721, USA. baheti@ece.arizona.edu
Neifeld
Mark A
MA
D000465:Algorithms; D005145:Face / Q000033:anatomy & histology*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008297:Male; D010363:Pattern Recognition, Automated