Abstract:

The traditional 3D face recognition and classification algorithms require multiple samples for training. However, the recognition performance will be seriously degraded on single sample training. To resolve the above problem, Fuzzy Adaptive Resonance theory MAP (Fuzzy ARTMAP) algorithm was used to classify the 3D face database. Firstly, the features of the 3D face deep image were extracted by Local Binary Pattern (LBP). Then the frequency-domain features of LBP features extracted by Log-Gabor wavelet were used as the input vectors for training. Finally the set of feature vectors were sent to Fuzzy ARTMAP classifier for recognition. The experiments compared with Probabilistic Neural Network (PNN) and Extreme Learning Machine (ELM) were conducted on FRGC v2.0 database, the recognition rate of the proposed algorithm reached 87.15%, the classifier training time was 24.88s, the matching time of single sample to single registered face was 0.0015s, and the searching time of a new face sample in the database was 1.08s. The experimental results show that the proposed method outperforms to PNN and ELM, it achieves a higher recognition rate with shorter training time, and has stable time performance with strong controllability.