Abstract:
The Fisherface method suffers from the problem
of using all training face images to recognize a test face
image. To tackle this problem, we propose combining a
novel clustering method, affinity propagation (AP), recently
reported in the journal Science, with linear discriminant
analysis (LDA) to form a new method, AP-LDA, for face recognition.
By using AP, a representative face image for each
subject can be obtained. Our AP-LDA method uses only
these representative face images rather than all training images
for recognition. Thus, it is more computationally efficient
than Fisherface. Experimental results on several
benchmark face databases also show that AP-LDA outperforms
Fisherface in terms of recognition rate.
