Objective

2

The development and popularization of face recognition authentication technology in recent years has made the storage of a large number of face photos in third-party servers highly common. Face recognition plays an important role in clothing

food

housing

and various industries

and moves from theoretical research to practical application of the "blowout period". However

faces are relatively open features compared to irises and fingerprints

and many people post selfies on various social platforms. Not only can you get face photos easily through the Internet

but you can also use a variety of image processing tools to fake faces. Thus

the protection of the privacy of face information has become prominent. At present

the research content in the field of face recognition focuses on directly recognizing face images

and there is a problem of privacy leakage; or the face image is encrypted and decrypted

but the encryption and decryption operation has the disadvantage of high computational complexity.

Method

2

To solve the problem of the unevenness of the face in a scrambled photo due to camera angles

this study preprocesses the face image as follows. First

we determine whether a given image contains a face. If a face does exist

then we find the border that contains the complete face. Next

we must locate the key points such as the nose and eyes

align the face images on the basis of these key point positions

and normalize them to the same size following the key mechanism of vision. That is

the human eye consistently sees the center of the photo first and then gradually moves to the last four corners. Then

the key parts of the face (eyes

ears

mouth

and nose) are scrambled and blocked by Arnold transform for a random number of times. Second

to achieve face privacy protection and image recognition after scrambling

this study proposes a deep convolutional neural network based on block random scrambling

which does not include an additional layer. The network structure of the model is composed of four convolutional layers

three pooling layers

one fully connected layer

and a softmax regression layer. The convolution kernel sizes of the four convolutional layers are 6×6

3×3

3×3

and 2×2. In the training phase

the preprocessed samples are divided into training sets and test sets. At the beginning of training

the convolution kernel parameters are randomly initialized to a small value

and small random numbers are used to ensure that the network does not enter a saturated state due to excessive weights. The training process is divided into the forward propagation and backward propagation phases. After the input passes by the multiple convolutional layers and pooling layers

it is transferred to the output layer. In the process

the input is actually multiplied by each layer of the weight matrix

and a calculation is performed to obtain the output result. The difference between the actual output and the ideal output is calculated in the backward propagation phase

and the weight is adjusted in reverse on the basis of the minimization error method. The server side directly verifies and recognizes the scrambled face image by the deep neural network model. Prior to transmission or storage on the server

the preprocessed and randomized scrambled images are encrypted

and the key is saved to further improve security. Then

the color histogram of the image will show a straight line. When identification is necessary and if a legal key is available

it can be correctly restored to the previous state to perform the identification operation.

Result

2

This algorithm enables the server to not store the original face template throughout the entire process

thereby achieving effective scrambling protection of the original face image. Using the block random scrambling proposed in this paper

a higher recognition rate can be obtained. Further considering the security problem

the image after random scrambling is twice encrypted and the key is saved before being transmitted or stored in the server. The experiment uses this deep convolutional neural network to identify the ORL face database

and the final recognition accuracy rate reaches 97.62%. Concurrently

the effectiveness of the proposed method is verified by multiple sets of comparative experiments. The face of the original image before processing has a strong correlation with adjacent pixels. After the pixel position is scrambled

the pixel points of the key positions of the face have a uniform distribution trend on the whole image

and the correlation is obviously weakened. Thus

the algorithm has a good effect on hiding the pixel points of the face.

Conclusion

2

Compared with other methods that are used to manually extract features and methods based on decision trees and random forest for training recognition in the literature

the proposed method reduces the workload of manually extracting features and retains a higher recognition rate. From the experimental results

the Arnold random parameter scrambling on the block image effectively reduces the correlation of the ciphertext image

and still maintains a high recognition rate for deep neural network recognition. This paper also uses the chaotic map encryption method for secondary encryption. The results show that the correlation of ciphertext images is further reduced

which not only enhances the protection of face privacy

but also has strong robustness to the image recognition after scrambling transformation.