De-identification Algorithms Of Facial Image Processing

With the continuous development of computer technology and artificial intelligence,the level of automation in human life has been continuously improved.At the same time,with the continuous advancement of the Internet of Things technology and big data technology,more and more information can be monitored and acquired.Today,whether you are entering or exiting a commercial building,or a public place,or even walking in a community where you live,your information may be obtained through IoT devices or various monitoring devices.Data shows that large-scale monitoring systems have played a powerful and positive role in controlling crimes.However,this information has also created some other problems while providing convenient services and full protection for humans.The protection of personal privacy in image information is one of the issues that need attention.Especially with the extensive applications of deep learning in the face recognition field,the recognition rate can already be comparable to the human level.Face recognition technology is booming and will be widely used in various fields,such as commercial advertising,public places,airports,and crimes.Most people may not realize that face recognition technology is developing by leaps and bounds,that is,it will bring convenience to people's scientific and technological life,and it will also bring great challenges to personal privacy.When you stand in front of a billboard with face recognition software,it knows who you are,what you like,your career,and more,using this information to recommend you ads that you might like.And as monitoring systems with face recognition technology widely used and connected,each of us will have nowhere to hide.Therefore,how can we protect our privacy in such an environment?Considering this problem,in this paper,de-identification algorithms of facial image processing are proposed.These algorithms not only provide effective facial images,but also protect personal identity information from leaking.The de-identification technology is to remove related information that can determine the foreground identities in image data,but to retain enough information to judge the foreground action behavior.The de-identification technology in image processing research is based on fuzzifying identity features to perform action recognition and behavior understanding.The data studied in this paper are facial images because human face is the most important source of identity information.In our process,we only focus on useful facial information,such as facial expressions,gender,and facial status.We need to encrypt or obscure the identity information.Based on the needs of practical problems,the main work of the paper are as follows:First of all,we summarize the related work of facial image processing based on cloud model during my doctoral period:feature extraction and expression of facial image based on cloud model,face recognition based on cloud model,face expression recognition and synthesis.The main purpose of these tasks is to use the three features of the cloud model:expectation(Ex),entropy(En),and hyper-entropy(He)to gain an in-depth understanding of facial images.It is expected that Ex is a mathematical expectation of a cloud drop that belongs to the general concept which can be considered as the most representative and typical sample of a qualitative concept.Entropy En is a measure of uncertainty in a qualitative concept which depends on the randomness and ambiguity of the concept.As a measure of randomness,En reflects the degree of dispersion of cloud droplets.On the other hand,it is also a measure of ambiguity,representing a range of domains that the concept can accept.The hyper-entropy He is the uncertainty of entropy En.Based on the cloud model,a de-identification algorithm is proposed.The core of the algorithm is to remove facial identity information while protecting facial expression information as much as possible.In order to verify the effectiveness of the algorithm,this paper conducts an experimental study on the JAFFE facial data set.It can be seen from the experimental analysis that this method makes it impossible for us to accurately recognize the identity of the person,but the facial expression information is preserved.We also propose another face de-identification algorithm based on Active Appearance Model(AAAM).In order to preserve the useful information of face images as much as possible while achieving the goal of privacy protection,we propose the algorithm,named k-Same-FSD,which combines feature subspace decomposition(FSD)technology and privacy protection method k-anonymity.Our basic precondition is that face images are integrated by various features such as identity,posture,expression,age and so on.Our k-Same-FSD method proves that the goal of privacy protection can be achieved by decomposing the face image space of a given image into "identity" subspace and "utility"subspace.In order to verify the performance of our proposed k-Same-FSD method,we conducted an experimental study on the Cohn-Kanade extended data set(CK +).The results show that this method can effectively protect privacy while also preserving utility information.Finally,based on the high performance of deep learning technology in the field of face recognition,we do the opposite.After the facial identity-preserving(FIP)feature is extracted by the convolutional neural network,we use our proposed privacy protection method to erase the identity features that can effectively recognize people's faces and keep other useful information as much as possible.Different from the traditional facial image feature representation,the FIP features can significantly reduce the differences among the face images of the same person while maintaining the distinction among different identities.By suppressing and modifying the FIP features,the method realizes the removal of face identity information while preserving useful facial feature information And it has been successfully demonstrated that the resulting "average face" will still maintain the beauty and usefulness of the original image while making the face recognition software ineffective.In summary,this thesis studied the face image de-identification technology to achieve the protection of personal privacy,while making the existing face recognition technology widely used in various fields friendly.