A novel fast k-nearest neighbor (k-NN) search method is proposed for the face identification task. It is well suited for this task because (1) it works well with high dimensionality, (2) it can be used with various similarity scores such as inner product, Euclidean distance, and correlation coefficient, (3) it can achieve not only fast exact k-NN search but much faster approximate search, and (4) it does not require any training or special data structure, resulting in low maintenance cost for the target database. Similarity scores between query and target samples are aggregated sequentially along with their dimensions, and target samples with no possibility of being included in k-NNs are rejected. The possibility is evaluated on the basis of the upper and lower bounds of the score for residual dimensions. Experimental results for a face database demonstrated that the proposed method achieves equal or better accuracy than other methods and is ten times faster than an exhaustive search with no degradation in the rank-k identification rate.