In this paper, we propose multimodal biometric feature fusion using alternating concatenation of DCT coefficients exist in face and plamprint images. Discrete cosine transform (DCT) is used to extract low frequency features which has high discrimination feature at the top left corner of the DCT transform image. The fuse feature vector is projected to the most principal component of eigenvector to produces low dimensional fused feature vector which contains important information about the face and palmprint images. Distance classifier is then implemented as a classifier to compute the nearest distance of test feature data point with a template to evaluate the recognition process. PolyU and FERET dataset is used to validate the propose method and the result shows fusion by using alternating concatenation of face and palmprint is able to produce a better recognition rates compare to concatenation method. The best recognition rate is 95%.
