An enhanced Local Ternary Patterns method for face recognition

Feature descriptor based methods (e.g. Local Binary Patterns, Local Ternary Patterns) have gained encouraging results in face recognition. However one needs to manually set the threshold in Local Ternary Patterns (LTP). The threshold in LTP is not data adaptive and not robust to noise. In some cases, we may not give a suitable threshold for LTP. Inspired by Weber´s Law, here a data adaptive threshold strategy is prosed for LTP and an enhanced LTP is given for face recognition. We evaluate the enhanced LTP on ORL and FERET face databases and the results demonstrate that the enhanced LTP significantly improves the performances.