Research And Application Of Improved Facial Landmark Detection Algorithm Based On CNN

With the continuous development of deep learning,face recognition technology plays an increasingly important role in the society.At present,face recognition technology has been applied to social life such as mobile phone unlocking,face scan payment,self-service supermarket,criminal arrests and so on.The first two parts of face recognition technology are face detection and face landmark detection.The accuracy of the face landmark detection will directly affect the result of facial recognition.In addition,face landmark detection can also be applied to areas such as expression analysis and face fusion.The face landmark detection is to automatically identify the feature points of the face area by an algorithm,such as: eye,nose,mouth,facial contour and other parts.Although the detection of face landmark has been applied to many fields,the detection of face landmark in the natural environment still has many problems.The uncontrolled natural environment such as light intensity,posture change and face occlusion is the challenge for detection.In this paper,several classic face landmark detection algorithms will be analyzed.the accuracy of these traditional algorithms will have a serious impact by the above problems.The main content of this article is:(1)By reading relevant literatures on key points detection at home and abroad,I understand the relevant algorithms and the status quo of deep learning research,and find the difficulties about detection of face key points,and then I determined the research content of this paper.(2)This paper introduces the basic theoretical knowledge of convolutional neural network(CNN),CNN optimization algorithm and deep learning framework.First,the CNN consists of a convolutional layer,a pooling layer,and an activation function.Then,introducing the forward propagation and reverberation propagation algorithms of CNN and the stochastic gradient descent algorithm.Finally,introducing the deep learning framework and training methods.(3)Through the experiment,comparing the existing face detection methods based on local detection,cascade-based regression detection and detection based on deep learning,and summarizing the problems of face key point detection.(4)Through the above analysis,this paper is concerned with the problem of single-layer CNN in dealing with multi-point key point detection.By making experiment and analyzing the existing model,a level from overall detection to local detection is proposed.The convolutional neural network structure first detects the key points of the peripheral contour of the face region,then accurately locates the internal five-point key points of the face,and compares it with other face key point detection algorithms.The experimental results show that the algorithm is better than other algorithms and has certain advantages.(5)Finally,the application of face sticker animation is realized.In summary,this paper improves the original convolutional neural network model by a whole-to-local detection method,which improves the face key point detection algorithm,and finally realizes the simple application of face key point detection.