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.

摘要：

针对传统的三维人脸识别分类算法大多需要多个样本进行训练，而在单训练样本的前提下识别性能会严重降低的问题，提出了基于模糊自适应共振理论映射(Fuzzy ARTMAP)的算法对三维人脸数据库进行分类识别。首先对三维人脸深度图像进行局部二值模式(LBP)统一模式算子的特征提取，再对LBP特征进行Log-Gabor小波变换，提取图像的频域特征向量作为训练的输入向量，最后将单样本训练向量集送入Fuzzy ARTMAP分类器进行训练识别。该算法在FRGC v2.0三维人脸数据库中的识别率可达到87.15%，分类器的训练时间为24.88s，单张待识别人脸样本与单张已注册的人脸匹配时间为0.0015s，一张新的人脸样本在数据库完成一次搜索匹配则需要1.08s。实验结果表明，所提方法在测试中的性能优于概率神经网络(PNN)和极限学习机神经网络(ELM)，既能保证较高的识别率，又能拥有较短的训练时间，且时间增幅稳定，可控性强。