基于MTCNN算法的多人脸识别研究

Research on Multi-Face Recognition Based on MTCNN Algorithm

随着人工智能在计算机视觉领域的飞速发展,越来越多的经典人工智能算法被应用于多人脸识别研究。其中,MTCNN算法在多人脸识别方面表现较好,但在识别精度上还有较大提升空间。本研究从经典的MTCNN算法框架出发,对其子算法NMS算法进行评估与改进,并对NMS算法与改进NMS算法在P-Net、R-Net、O-Net各个级联网络中的表现差异进行比较与理论分析。对改进后的NMS算法进行主观和客观相结合、横向比较与纵向比较相结合的多种维度方式的评估与鉴别。实验结果表明,本研究设计的模型在数据集LFW上的人脸识别准确率为94.56%,可为多人脸识别研究提供参考。

With the rapid development of artificial intelligence in the field of computer vision,more and more classical artificial intelligence algorithms are applied to multiple face recognition research.Among them,the MTCNN algorithm performs well in multi-face recognition,but there is still a relatively large space for improvement in recognition accuracy.In this study,based on the classical MTCNN algorithm framework,the refinement of its subalgorithm NMS algorithm was evaluated and improved.The performance differences between the NMS algorithm and the improved NMS algorithm were compared and theoretically analyzed in each cascade network of P-Net,R-Net,and O-Net.The improved algorithm was evaluated and identified in multiple ways combining subjective and objective and horizontal comparison and longitudinal comparison.The results showed that the model designed in this paper achieves a face recognition accuracy of 94.56%on the LFW dataset.It can provide a reference for multi-face recognition.