Open AccessHigh-Precision Portrait Classification Based on MTCNN and Its Application on Similarity Judgement
Author(s) -
Juan Du
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1518/1/012066
Subject(s) - computer science , convolutional neural network , artificial intelligence , face (sociological concept) , task (project management) , similarity (geometry) , judgement , pattern recognition (psychology) , deep learning , machine learning , face detection , object detection , image (mathematics) , facial recognition system , social science , management , sociology , political science , law , economics
Portrait classification is a complex course including at least face detection, recognization and compare each of which contains multi-tasks, facing plenty of various challenging questions due to askew poses, illuminations, occlusions, image blurring and small scale face in the pictures. Though deep learning methods, such as Convolutional Neural Network (CNN) family and You Only Look Once (YOLO) series, had boomed a large number of areas on object detection and accelerated the solving of these difficulties on image processing, they are not specially designed for the image classification and may require a great deal of resource, expensive computation and taxing annotation. In 2016, an innovative face detection model named Multi-task convolutional neural network (MTCNN) arose and triggered viral and wide spread. Its high efficient and accurate performance on both face detection and face alignment tasks, real time effect based on lightweight CNN as well as effective conducting online hard sample mining, all contribute to significant improvement to the challenges above. This paper introduces the MTCNN algorithm and applies it to the similarity judgement with two industrial real problems together with FaceNet model. In addition, some effective practical methods on increase precision of classification are also proposed to gain better effect.