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Fisherfaces algorithm is a popular method for face recognition. However, there exist some unstable components that degrade recognition performance. In this paper, we propose a method based on detecting reliable components to overcome the problem and introduce it to 3D face recognition. The reliable components are detected within the binary feature vector, which is generated from the Fisherfaces feature vector based on statistical properties, and is used for 3D face recognition as the final feature vector. Experimental results show that the reliable components feature vector is much more effective than the Fisherfaces feature vector for face recognition.