New Approach for Preventing Unauthorized Access and Key Generation from Facial Data

New Approach for Preventing Unauthorized Access and Key Generation from Facial Data

Dr. Kamini Solanki

Assistant Professor,

Faculty of IT & CS, Parul University Vadodara, India

Abstract: Face Recognition is used for real time application and demanded application. This paper represents two modules. In first module, face recognition is done by combining local binary pattern (LBP) and principal component analysis (PCA) in different way. Proposed algorithm is used for better recognition rate. PCA is used for dimension reduction of image and LBP is used to describe the texture of image data. So hybrid approach will increase the recognition rate of face and also decreased false match rate but there is no difference in verification time. So it is suitable for real time application. We compared proposed method with both PCA and LBP to compute these changes. In case of execution time, there is no difference between of existing and proposed method. In Second module, key generation from feature vectors of proposed algorithm for the purpose of the security. According to the result proposed algorithm generate fastest key compared to the existing key.

Key words: Facial image representation, LBP, PCA, Recognition rate, False match rate, Key Generation, Encryption, Decryption

1. INTRODUCTION

   Face Recognition features can be done by global features and local features of face. Global features focus on the entire facial image so it has Less Accuracy. Local Features focus only local area of the face, which is help to identify and verify the person. So it is most accurate method. The local binary pattern (LBP) is design for local texture description and shape of an image. It is done by dividing an image into small parts from which local features are extracted. These local features consist of binary patterns which describe the location of pixels in those areas. The gained features from the regions are combined into a single feature histogram, which is represents the image. Images can be compared by distance measurement. It is based on local feature extraction and dimension reduction. In proposed method face recognition done by combining the LBP and PCA in different way which provides very good results. It increased the performance of recognition. It can work against face images with different facial conditions.

   Fig.1 Face recognition methods

2. FACE RECOGNITION PROBLEM USING PCA

   ALGORITHM

   1. Illumination Problem

      Illumination problem happens when same image with condition. So person have to keep with fix lighting condition, fixed distance, same facial expression and also same view point. It can emerge extensively different when lighting condition is different. [8]

   2. Pose Problem

      Face recognition with different facial poses that is called pose problem. If face rotation made very large changes in face appearance it reduce recognition rate. If person try to match same image with different facial pose, it show the different result.[8]

3. FACE RECOGNITION PROBLEM USING LBP

   OPERATOR

   Limitation of the basic LBP operator is that its small 3 * 3 neighborhood cannot capture dominant features with large- scale structures. It cannot deal with the texture at different scales and the operator was later generalized to use neighborhoods of different size so LBP cannot work well on large scale images.[5]

4. DATABASE

   The FEI face database is a Brazilian face database that holds 14 images for each of 200 individuals, a total of 2800 images. All images are colorful and standing frontal position with profile rotation up to about 180 degrees. Age of persons between 19 to 40 years old with distinct appearance, hairstyle, and adorns.[47]

5. OBJECTIVE OF PROPOSED METHOD

   • Increased recognition rate

   • Decreased false mate rate

   • Biometric template security using cryptography

   • Fastest key generation from facial feature

6. LOCAL BINARY PATTERNS

   Local Binary Pattern (LBP) operator describes the texture and shape of a digital or gray scale image. LBP is a binary code for an image-pixel which describe local neighborhood of that pixel. This operator works with the eight neighbors of a pixel, using the value of this center pixel as a threshold. If a neighbor pixel has a higher gray value than the center pixel (or the same gray value) than a one is assigned to that pixel, else it gets a zero. The LBP code for the center pixel is then produced by concatenating the eight ones or zeros to a binary code.[43]

   Fig. 2 The original LBP operator

   Later the LBP operator can be extended. You can increase neighborhoods of different sizes. In that situation a circle is made with radius R from the center pixel. P sampling points on the edge of this circle are taken and compared with the value of the center pixel. To get the values of all sampling points in the neighborhood for any radius and any number of pixels, for neighborhoods the notation (P, R) is used. Fig 3 illustrates three neighbor-sets for different values of P and R and also called as multi scale LBP or extended LBP.[43]

   Fig. 3 circularly neighbor-sets for three different values of P and R. LBP on FEI Database

   Fig. 4 LBP on FEI Database

7. PCA ALGORITHM

   Principal Component Analysis (PCA) is well-organized method for face recognition. It is one of the most usable methods for a face image. It is used to reduce the dimensionality of the image and also holds some of the variations in the image data. It is projecting face image data into a feature space that covers the significant variations among known facial images. Those significant features are known as Eigen faces, because they are the eigenvectors or Principal Component of the set of faces. That is not necessary to correspond to the features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the Eigen faces features. So to recognize a particular face, it is necessary only to compare these weights to those individuals. The Eigen Object Recognizer class applies PCA on each image, the results of which will be an array of Eigen values. To perform PCA several steps are undertaken: [8][20][19] Stage 1: Subtract the Mean of the data from each variable (our adjusted data) subtraction of the overall mean from each of our values as for covariance we need at least two dimensions of data. It is in fact the subtraction of the mean of each row from each element in that row.

   Stage 2: Calculate and form a covariance Matrix

   Stage 3: Calculate Eigenvectors and Eigen values from the covariance Matrix Eigen values are a product of multiplying matrices however they are as special case. Eigen values are found by multiples of the covariance matrix by a vector in two dimensional space (i.e. a Eigenvector). This makes the covariance matrix the equivalent of a transformation matrix.

   Stage 4: Chose a Feature Vector (a fancy name for a matrix of vectors) Once Eigenvectors are found from the covariance matrix, the next step is to order them by Eigen value, highest to lowest. This gives you the components in order of significance. Here the data can be compressed and the weaker vectors are removed producing a lossy compression method, the data lost is deemed to be insignificant.

   Stage 5: Multiply the transposed Feature Vectors by the transposed adjusted data. The final stage in PCA is to take the transpose of the feature vector matrix and multiply it with the transposed adjusted data set (the adjusted ata set is from Stage 1 where the mean was subtracted from the data).

   PCA on FEI Database

   Fig. 5 PCA on FEI Database

8. COMPARISON OF PCA & LBP ALGORITHM

   PCA Algorithm	LBP Algorithm

   • High Recognition Rate

   • Global feature extraction

   • Fastest execution time

   • Suitable for real time application

   • Illumination problem removal

   • Local feature extraction

9. PROPOSED ALGORITHM

   Step 1: Find the mean image.

   Step 2: Reallocate/instantiate array for the local binary pattern.

   Step 3: LBP feature extraction using Feature Vectors. Step 4: Calculate the ordered eigenvectors and eigenvalues

   Step 5: Combine LBP local feature vectors with PCA global feature vectors.

   Step 6: Key generation using these face features and encrypt image data using Euclidean and Euclidean Squared Distance Metrics method.

   Step 7: Encrypted image data stored in database.

   Step 8: Apply Step 1 to Step 6 if it is Inputted Image. Step 9: Decrypt Database all image data using same key.

   Step 10: Verification of Database images and inputted image using Ecudian distance measurement method.

   Step 11: Retrieved image from database which have minimum distance between input image and Database images.

10. IMPLEMENTATION

    1. Implementation of proposed algorithm

       Fig.6 Implementation of proposed algorithm

    2. Key Generation and Implementation of image cryptography

    Fig. 7 Implementation of proposed algorithm on real time database

    Subject	DB Images	Trainee Images	RR%		FMR%		Avg.Verification Time (In Seconds)
    			PCA	LBP	PCA	LBP	PCA	LBP
    05	14	14	98.50	62.85	1.5	37.15	0.37	0.05
    10	14	14	95.71	70.00	4.29	30.00	0.25	0.05
    15	14	14	92.85	79.52	7.15	20.48	0.29	0.05
    20	14	14	89.64	64.64	10.36	35.36	0.32	0.05

    Subject	DB Images	Trainee Images	RR%		FMR%		Avg.Verification Time (In Seconds)
    			PCA	LBP	PCA	LBP	PCA	LBP
    05	14	14	98.50	62.85	1.5	37.15	0.37	0.05
    10	14	14	95.71	70.00	4.29	30.00	0.25	0.05
    15	14	14	92.85	79.52	7.15	20.48	0.29	0.05
    20	14	14	89.64	64.64	10.36	35.36	0.32	0.05

11. RESULT ANALYSES OF PCA AND LBP ALGORITHMS Table 1. PCA & LBP on FEI Face Database

    Table 2. PCA& LBP on Real time Database (6 trainee images)

    Subject	DB Images	Input Images	RR%		FMR%		Avg.Verification Time (In Seconds)
    			PCA	LBP	PCA	LBP	PCA	LBP
    05	5	6	76.66	52.22	23.34	47.78	0.22	0.04
    10	5	6	63.33	48.66	36.67	51.34	0.25	0.05
    15	5	6	58.88	46.66	41.12	53.34	0.27	0.05
    20	5	6	56.66	46.55	43.34	53.45	0.30	0.06

    Table 3. PCA& LBP on Real time Database (14 trainee images)

    Subject	DB Images	Input Images	RR%		FMR%		Avg.Verification Time (In Seconds)
    			PCA	LBP	PCA	LBP	PCA	LBP
    05	5	14	82.85	68.00	17.15	32.00	0.04	0.04
    10	5	14	75.71	65.00	24.29	35.00	0.05	0.05
    15	5	14	76.66	70.47	23.34	29.53	0.05	0.06
    20	5	14	75.71	69.64	24.29	30.36	0.06	0.06

    Table 4 proposed system on real time database (14 trainee images)

    Subject	Database Images	Input Images	Recognition Rate (RR in %)	False Match Rate (FMR in %)	Avg.Verification Time (In Seconds)
    05	5	14	92.85	7.15	0.04
    10	5	14	90.71	9.29	0.06
    15	5	14	84.28	15.72	0.04
    20	5	14	84.64	15.36	0.07

    Table 5 proposed system on real time database (3 DB images)

    Subject	Database Images	Input Images	Recognition Rate (RR in %)	False Match Rate (FMR in %)	Avg.Verification Time (In Seconds)
    05	3	14	88.57	11.43	0.03
    10	3	14	79.28	20.71	0.04
    15	3	14	71.90	28.10	0.05
    20	3	14	72.85	27.15	0.05

    Table 6 Comparison (Difference) of proposed algorithm with PCA and LBP

    Subject	DB Images	Input Images	Increased RR%		Decreased FMR%		Diff. AVG.Veri.Time (In Seconds)
    			PCA	LBP	PCA	LBP	PCA	LBP
    05	5	14	10.00	24.85	10.00	24.85	00.00	00.00
    10	5	14	15.00	25.71	15.00	25.71	00.01	00.01
    15	5	14	07.62	13.81	07.62	13.81	00.01	00.02
    20	5	14	08.93	15.00	08.93	15.00	00.01	00.01

    Table 7 Key Generation, Encryption, and Decryption of existing algorithm

    Subject	DB Images	Input Images	Key Generation	Encryption	Decryption
    			(In seconds)
    05	14	14	0.05	0.15	0.14
    10	14	14	0.05	0.14	0.14
    15	14	14	0.06	0.14	0.14
    20	14	14	0.08	0.22	0.15

    Table 8 Key Generation, Encryption, and Decryption of proposed algorithm

    Subject	DB Images	Input Images	Key Generation	Encryption	Decryption
    			(In seconds)
    05	14	14	0.00	0.12	0.12
    10	14	14	0.00	0.12	0.12
    15	14	14	0.00	0.13	0.11
    20	14	14	0.00	0.13	0.11

    Table 9 Difference between Key Generation, Encryption, and Decryption of existing algorithm and proposed algorithm

    Subject	DB Images	Input Images	Key Generation	Encryption	Decryption
    			(Decreased In seconds)
    05	14	14	0.05	0.03	0.02
    10	14	14	0.05	0.02	0.02
    15	14	14	0.05	0.01	0.03
    20	14	14	0.05	0.09	0.04

12. CONCLUSION

LBP is fastest execution operator so it is most suitable for real time application. LBP feature vector used to remove illumination problem but it works only on local regional of image. So it cannot capture dominant features of large- scale structures. PCA has high accuracy rate but it has illumination and pose problem. If LBP feature vector combine with PCA Eigen vector according to this way, it remove the illumination and pose problem and also increased recognition rate and decreased false match rate as well as not much more difference between verification time. For the security purpose, image data is encrypted with the proposed feature vectors so it is used for the security of database template.

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