(Luận văn thạc sĩ) nhận diện mống mắt dùng xử lý ảnh

BỘ GIÁO DỤC VÀ ĐÀO TẠO TRƯỜNG ĐẠI HỌC SƯ PHẠM KỸ THUẬT THÀNH PHỐ HỒ CHÍ MINH LUẬN VĂN THẠC SĨ NGUYỄN PHÚC VIÊN NHẬN DIỆN MỐNG MẮT DÙNG XỬ LÝ ẢNH NGÀNH: KỸ THUẬT ĐIỆN TỬ - 60520203 SKC005836 Tp Hồ Chí Minh, tháng 04/2018 BỘ GIÁO DỤC VÀ ĐÀO TẠO TRƢỜNG ĐẠI HỌC SƢ PHẠM KỸ THUẬT THÀNH PHỐ HỒ CHÍ MINH LUẬN VĂN THẠC SĨ NGUYỄN PHÚC VIÊN NHẬN DIỆN MỐNG MẮT DÙNG XỬ LÝ ẢNH NGÀNH: KỸ THUẬT ĐIỆN TỬ - 60520203 Tp Hồ Chí Minh, tháng năm 2018 BỘ GIÁO DỤC VÀ ĐÀO TẠO TRƢỜNG ĐẠI HỌC SƢ PHẠM KỸ THUẬT THÀNH PHỐ HỒ CHÍ MINH LUẬN VĂN THẠC SĨ NGUYỄN PHÚC VIÊN NHẬN DIỆN MỐNG MẮT DÙNG XỬ LÝ ẢNH NGÀNH: KỸ THUẬT ĐIỆN TỬ - 60520203 Hƣớng dẫn khoa học: TS NGUYỄN THANH HẢI Tp Hồ Chí Minh, tháng năm 2018 i ii LÝ LỊCH KHOA HỌC I LÝ LỊCH SƠ LƢỢC: Họ & tên: Nguyễn Phúc Viên Giới tính: Nam Ngày, tháng, năm sinh: 07/06/1990 Nơi sinh: Phú Yên Q qn: Đơng Hịa – Phú n Dân tộc: Kinh Chỗ riêng địa liên lạc: Phú Lƣơng – Hịa Tân Đơng – Đơng Hịa – Phú n Điện thoại nhà riêng: 096 454 89 86 E-mail: nguyenphucvien@gmail.com II QUÁ TRÌNH ĐÀO TẠO: Đại học: Hệ đào tạo: Chính Qui Thời gian đào tạo từ 09/2008 đến 04/2012 Nơi học (trƣờng, thành phố): Đại Học Sƣ Phạm Kỹ Thuật TP HCM Ngành học: Công Nghệ Kỹ Thuật Máy Tính Tên đồ án, luận án mơn thi tốt nghiệp: Mobile Robot Ngày & nơi bảo vệ đồ án, luận án thi tốt nghiệp: 08/2012 Ngƣời hƣớng dẫn: Ths Trƣơng Ngọc Sơn BI Q TRÌNH CƠNG TÁC CHUYÊN MÔN KỂ TỪ KHI TỐT NGHIỆP ĐẠI HỌC: Thời gian iii LỜI CAM ĐOAN Tôi cam đoan cơng trình nghiên cứu tơi Các số liệu kết nghiên cứu luận văn trung thực chƣa đƣợc công bố cơng trình khác Mọi giúp đỡ cho việc thực luận văn đƣợc cảm ơn thơng tin trích dẫn luận văn đƣợc ghi rõ nguồn gốc Tác giả luận văn iv CẢM TẠ Xin chân thành gửi lời cảm ơn đến TS Nguyễn Thanh Hải tận tình hƣớng dẫn tơi thời gian thực chuyên đề Xin chân thành gửi lời cảm ơn đến tồn thể q thầy trƣờng Đại học Sƣ Phạm Kỹ Thuật TP Hồ Chí Minh giảng dạy, hƣớng dẫn tạo điều kiện cho tơi có mơi trƣờng học tập tốt Cảm ơn ba mẹ, anh chị em bạn bè giúp đỡ, động viên suốt thời gian học Xin kính chúc sức khỏe chân thành cảm ơn Học viên Nguyễn Phúc Viên v TÓM TẮT Hệ thống nhận diện mống mắt hệ thống xác thực sinh trắc xác Một đặc tính mống mắt hình thành cấu trúc cách ngẫu nhiên từ chi tiết nhỏ Biểu ngẫu nhiên khác chí thể hai mống mắt có gen di truyền giống nhƣ cặp song sinh giống hệt Cách cấu tạo mống mắt khơng có biểu di truyền mà hỗn loạn Việc nhận diện xác định danh tính mống mắt ngày đƣợc áp dụng rộng rãi nhiều ứng dụng xã hội từ ứng dụng nhƣ bảo mật điện thoại thông minh đến ứng dụng nhƣ xác định danh tính, hộ tịch, tài khoản ngân hàng v.v Các thuật tốn đƣợc trình bày chun đề để nhận diện hay xác định danh tính ngƣời mơ hình mống mắt họ Từ ảnh mắt, sử dụng tốn tử Daugman để tìm kiếm mống mắt ảnh Mống mắt đƣợc tách ảnh riêng, chứa ảnh mống mắt việc áp dụng mơ hình cao su (Daumag ruber sheet model) Daugman Sau đó, mẫu mống mắt đƣợc trích đặc trƣng qua phân tích wavelet hai chiều, lúc ảnh giữ lại đặc điểm đặc trƣng mống mắt ngƣời Ảnh chứa đặc trƣng mống mắt ngƣời lƣu vào sở liệu để làm mẫu đối chiếu so sánh sau Hoặc trƣờng hợp nhận diện xác định danh tính, mẫu mống mắt chứa đặc trƣng đƣợc đem so sánh với mẫu mống mắt sở liệu đƣợc đăng ký trƣớc đó, phƣơng pháp so sánh khoảng cách Hamming Khoảng cách Hamming thể tƣơng đồng hai mẫu mống mắt, dựa vào điều xác định đƣợc kết nhận diện vi ABSTRACT The iris recognition system is an accurate biometric authentication system The characteristic of the iris is the formation of structures randomly from small details It can express of the difference between two irises of the same gene that the looks like identical twins The pattern of the iris is not genetic and it is the chaos Identification using iris is increasingly in many applications, such as security in smartphones, identitying verification, civil status, bank account etc The algorithms are presented in this paper to identify iris of people using one iris model The principle of iris recognition is the failure of the statistical independence test for the structure of the iris that has been deduced by the twodimensional wavelet model From an eye image, one can use the Daugman operator to find an iris The iris image will be separated fom an eye image using Daugman's Daumag ruber sheet model After that, the iris pattern is characterized by twodimensional wavelet analysis, which retains only the features of the iris For identification, the iris image is processed and compared to the sample image using the Hamming distance vii 39 S1008R02 S1011R04 Từ kết so sánh đƣợc cho thấy khả quan việc xác định danh tính ngƣời mống mắt họ Việc nhận diện ngƣời ngƣời khơng có nhầm lẫn Chỉ có trƣờng hợp ảnh bị mi mắt hay lông mi che khuất, làm khác cấu trúc mống mắt so với lúc đăng ký mẫu mống mắt ban đầu khơng xác định đƣợc danh tính Lúc cần yêu cầu ngƣời đƣợc xác định danh tính lấy lại mẫu mống mắt đƣợc Việc tƣơng tự nhƣ công dân rút tiền ngân hàng nhà nƣớc, đƣợc yêu cầu ký lại tên hay in lại dấu vân tay không xác định đƣợc Ngƣỡng T đƣợc đặt cho việc phân loại tiến độ xác cao, nhƣng điều đồng nghĩa với việc lấy ảnh đầu vào phải giống với mẫu đƣợc đăng ký trƣớc , mẫu A B tƣơng đồng , mẫu A B mẫu khác 40 Chƣơng KẾT LUẬN 4.1 Kết luận Ngƣời thực hồn thành đƣợc nội dung đề tài đƣa nhận diện đƣợc mống mắt từ ngƣời mống mắt từ ngƣời khác Tác giả thu thập sở liệu mống mắt, sau sử dụng phƣơng trình vi tích phân Daugman để xác định đƣợc tâm bán kính ngƣơi, nhƣ tâm bán kính mống mắt Sử dụng mơ hình cao su Daugman thực đƣợc việc tách mống mắt khỏi ảnh mắt nhƣ đƣa dạng chuẩn hóa Đã trích đặc trƣng mống mắt sử dụng phân tích wavelet hai chiều Thực biến đổi nhị phân sau trích đặc trƣng tiến hành so sánh nhận diện phƣơng pháp so sánh khoảng cách Hamming Kết nhận diện mẫu mống mắt ngƣời khác có phân biệt Tuy nhiên, chƣa thực loại bỏ vùng ảnh không cần thiết nhƣ bị mí mắt che phủ ảnh đầu vào Nên kết nhận diện mống mắt ngƣời bị thất bại Bên cạnh đó, đề tài chƣa tự xây dựng sở liệu ảnh đầu vào để có ảnh đầu vào đƣợc đồng Vì việc nhận diện mống mắt đƣợc áp dụng vấn đề an ninh bảo mật, nên yêu cầu lấy ảnh mống mắt đầu vào lúc đăng ký định danh lúc nhận diện phải có tƣơng đồng, thống 4.2 Hƣớng phát triển đề tài Để phát triển đề tài tác giả đề xuất sử dụng mạng neural để thực trình nhận diện Khi cho nhiều mẫu mống mắt ngƣời cho mạng neural học, nhận diện tăng khả xác định trùng khớp Đó cách để giảm việc phải lấy lấy lại ảnh mắt tiến hành xác định danh tính, hay nhận diện công dân 41 TÀI LIỆU THAM KHẢO [1] M Choras, "Emerging Methods of Biometrics Human Identification," Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007), Kumamoto, pp 365-365, 2007 [2] Xiaoli Zhou and B Bhanu, "Integrating Face and Gait for Human Recognition," the Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06), pp 55-55, 2006 [3] I V Anikin and E S Anisimova, "Handwritten signature recognition method based on fuzzy logic", Dynamics of Systems, Mechanisms and Machines (Dynamics), Omsk, pp 1-5, 2016 [4] S K Bandyopadhyay, D Bhattacharyya and P Das, "Handwritten signature recognition using departure of images from independence," 2008 3rd IEEE Conference on Industrial Electronics and Applications, Singapore, pp 964-969, 2008 [5] S Mil'shtein, A Pillai, A Shendye, C Liessner and M Baier, "Fingerprint Recognition Algorithms for Partial and Full Fingerprints," 2008 IEEE Conference on Technologies for Homeland Security, Waltham, MA, pp 449-452, 2008 [6] Adler, F.H., “Physiology of the Eye” (Chapter VI, page 143), Mosby, 1953 [7] Leonard Flom and Aran Safir, “Iris recognition system”, Patent Publication number: US4641349 A, 1987 [8] John G Daugman, Hungtindon, “Biometric personal indentification system base on iris analysis”, United States patent, Patent number: 5,291,560, 1994 [9] Richard P Wildes, “Automated non-invasive iris recognition system and method", United States patent, Patent number: 5,572,596, 1996 [10] Mitsuji Matsushita, “Iris identification system and iris identification method”, United States patent, Patent number: 5,901,238, 1999 42 [11] S Patil, S Gudasalamani and N C Iyer, "A survey on Iris recognition system," 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, pp 2207-2210, 2016 [12] J Daugman, "How iris recognition works," in IEEE Transactions on Circuits and Systems for Video Technology, vol 14, no 1, pp 21-30, 2004 [13] Nguyễn Thanh Hải Giáo trình xử lý ảnh NXB Đại học quốc gia TP HCM, 2014 [14] Jun ZHOU, Ting LUO, Min , Shijun GUO, Taiping QING, “Using 2D Haar Wavelet Transform for Iris Feature”, Asia-Pacific Conference on Information Theory, 2010 43 PHỤ LỤC BÀI BÁO KHOA HỌC DWT algorithm for Iris Recognition Nguyen Phuc Vien1, Nguyen Thanh Hai1 and Ngo Duc Dat2 Faculty of Electrical-Electronics Engineering, HCMC University of Technology and Education, Vietnam HCMC Navy Technical College, Vietnam Abstract This paper proposes with a Discrete Wavelet Transform (DWT) method to extract features for iris recognition In particular, Daugman‟s Integro - differential operator is applied to extract iris image from human eye image and the iris image is analyzed to extract features using the DWT for iris recognition of one person From the iris features, a threshold method is proposed to estimate similarity between irises of people for recognition of one corresponding person Results show that contribution of this research illustrates the effectiveness of the human recognition method Keywords: Iris recognition, Discrete wavelet transform, biometrics, Daugman‟s Integro - differential operator Introduction In recent years, man systems for human identification based on signals or images have been developed with increased reliability In particular, different ways of identification of voices, faces, obstacles, eyes and others people have attracted researchers [1, 2] Identification of iris part in human eye image for recognition of one corresponding person is one interesting issue Biometrics are the reliable and secure instrument for access control systems and physical assets provided by individual characteristics or based on physiological or behavioral characteristics [3, 4] Related to human recognition of characteristics, particularly the physiological characteristics are iris, fingerprint, face and hand geometry or behavioural characteristics consist of voice, signature, gait and keystroke dynamics [5, 6] Moreover, methods can be applied for biometric recognition based on properties and they cannot be forgotten or stolen like traditional authentication such as passwords or PIN‟s [7, 8] To successfully perform iris recognition, iris segmentation in eye image is a very important [9] Two methods, which are often used for iris segmentation, are Wildes method and Daugman‟s one In particular, Wildes proposed the iris segmentation with two steps: firstly, eye image is converted into binary image base on gradient of intensities of the pixels in an iris image; the secondly, the iris inner and outer borders are detected using Hough transform [10] Daugman‟s algorithm is an integro differential operator that allows to search over an eye image for the circular pupil and borders of the iris image [11] Therefore, the circular edge is detected for determining parameters of circular border 44 The performance of an iris recognition system is affected by iris features In recent decade, 2D Gabor filters developed by Daugman have been applied for filtering noises of images Therefore, Wavelet transform algorithm are employed for feature extraction [12] It means that this is one of the methods is applied for improvement of the human iris recognition system Some other research results showed that the method of identifying human iris is highly accurate compared to that of biometric fingerprint identification In particular, the structure of the human iris has 240 distinct characteristics compared to only 20 to 40 fingerprint recognition features [13] It means that using the structure of the iris for recognition is more accurate than that of the fingerprint In this paper, the threshold method is proposed to estimate iris recognition of people based on eye images In addition, this research shows statistics of many iris images of different people and between two eyes of one human for estimating the effectiveness of the proposed method This paper is organized as follows: Section describes the materials and methods related to the DWT and DIDO, in Section 3, results and discussion of iris recognition are obtained, Section provides the overall conclusion Materials and Methods Collection Extraction of Normalizatio of eye iris image n of iris Choosing Feature Threshold extraction Decision Fig Block diagram of iris recognition The collection of eye image with iris for identification is one of the major challenges due to requiring its high-quality image and they are not obscured by human eyelashes In addition, in this research, the eye image database with iris is obtained from the website of the organization of Biometrics Ideal Test (CASIA-IrisInterval) and there are 249 persons with one left eye or one right eye only Which each eye image with an iris has the resolution of 320x280 pixels From these eye images, an iris image of each eye image needs to extract for recognition, then the methods of normalization and feature extraction for estimating and decision are employed as shown in Fig 2.1 Extraction of iris image The algorithm of Daugman‟s Integro-Differential Operator (DIDO) [5] is applied to find an iris image in an eye image as described: max ( r,x ,y0 ) various circular region with the center coordinate at (x , y ) , σ 45 is the standard deviation of the Gaussian distribution, Gσ (r) denotes the Gaussian filter of the scale sigma ( σ ), (x , y ) is the assumed centre at the iris coordinate and s is the contour of the circle determined by the parameters of (r, x , y ) In Daugman‟s Operator, a Gaussian filter is employed to make smooth image and to reduce noise of eye image In order to find an iris image in an eye image, one needs to set up parameters as described in Table From these parameters, circles of pupil and iris are drawn as shown in Fig Therefore, in order to calculate the iris, the iris image needs to be normalized by using the model as shown in Fig Fig Representation of finding an Iris image in an eye image Table Description of parameters of finding the iris image Pupil Iris 2.1 Normalization of iris image After determining the iris area in the eye image, all iris images need to be resized for comparison The spatial conflict between eye images is mainly due to dilated iris from different lighting levels In particular, the main causes of inconsistencies include the projecting distance, the rotations of the camera and the eye in the eye socket Thus, the normalization of iris image is necessary and the normalization process will produce the same irregularly shaped iris areas With this iris normalization, two images with the same iris under different conditions will have the same structure at the same locations Fig Daugman‟s rubber sheet model 46 The homogenous rubber sheet model was devised by Daugman [14, 15], in which each point within the iris region corresponding to a pair of polar coordinates is (r, θ ) , where r is on the interval [0,1] and θ is the angle of [0,2π ] This model allows to convert an iris image into a homogenous rubber sheet image as described in Fig From this model, the rubber sheet used for remapping of the iris image can be represented as follow: I ( x( r , θ ), y ( r , θ )) → I ( r, θ) where x( r , θ ) = (1 − r ) x p (θ ) + rxl (θ) y ( r , θ ) = (1 − r ) y p (θ ) + r y l (θ) Fig Normalization of the iris image From Operator (2), an iris image is calculated and normalized to be a rubber sheet image as shown in Fig Therefore, all iris images after normalization are calculate to extract features for iris recognition 2.3 Discrete wavelet transform algorithm for feature extraction The Discrete Wavelet Transform (DWT) algorithm id applied to analyze features in iris regions into components appearing at different resolutions [16] The DWT allows to collect coefficients for extracting features of iris images Therefore, the coefficient output of the DWT is encoded to provide a compact and distinctive representation of the iris model and its equations are described as follows: Wψi = 47 Fig An Iris image is analysed using the discrete wavelet transform Fig shows the result of the iris image using the DWT with Harr function, in which the approximate image contains features After extracting features, the iris image needs to be encoded in binary Thus, the DWT algorithm was employed at level-2 to produce three coefficient components H, V, D which can be utilized for binary coding by using the following equation: C (i) = 0,   C (i ) = 1, where C represents the iris feature space after the DWT, C= {LH3, HL3, HH3} after the DWT with level 3, and C(i) is the element of C 2.4 Threshold algorithm for iris identification From features of the iris image, the Hamming Distance (HD) indicates the number of bits that are the same between two bit patterns Using the HD of the two bit patterns, one decision can be made whether two samples of different irises or from they are the same iris It means that when one compares between samples A and B, the HD(A, B) is defined as the sum of the bits not to be the same per the total number of bits of a sample and its equation is described as follows: HD(A, B) = in which N is the size of an iris feature code, A and B are denoted as different iris feature codes, Aj and Bj are corresponding bits of the iris feature codes For evaluating the similarity of two iris samples, the Similarity Degree (SD) method is applied and its equation is defined as follows: SD(A, B) =1− HD(A, B) In general, the SD and HD are the same, but the SD is the same direction to the similarity of the two irises Finally, the threshold T is employed for estimating, particularly if SD(A, B) ≥ T , then A and B comes from an iris and else that is not Results and Discussion In this research, each iris of one person is encoded to be S1011L08, in which S1011 is assigned a person; letters of L or R are assigned to be the left iris or right iris; 08, 09 or 02 is the number of the sample Therefore, the algorithm was applied to calculate values of SD on the iris images and one is based on it to 48 estimate and make decision The results in Table show that the SD value of Iris from two eyes is always less than 0.625, so the threshold T=0.625 can be installed to identify matching In particular, the SD values, which are smaller than T, mean that two iris images are estimated not to be the same and inversely it is the same Therefore, to be able to select an optimal T threshold, the database with different iris samples is large Table The results of matching iris images of the left eye images between different samples Matching Images Results SD=0.628 Iris A-S1011L02 SD=0.6308 Iris A-S1011L02 SD=0.6276 Iris B-S1011L08 Iris A-S1011L09 In case of two irises of different people as shown in Table 2, in which the SD values are considered differently for recognition In similarity, Table shows matching an iris image compared with that of other iris images corresponding to the different SD values Table The results of matching iris images with different people Matching Image Result SD=0.6176 Iris A- S1011L02 49 SD=0.6215 Iris A-S1011L08 SD=0.6201 Iris A-S1011L02 In Table 4, two iris images show the difference in structure of the left and right iris images of one person corresponding to one SD value Table The results of matching with left iris with right iris of a person Matching Image Result SD=0.6184 Iris A- S1011L02 From the above results, with SD=0.6184 < T, it shows that there is the difference in the structure of the iris images between the left and the right iris images Therefore, one can be based on this difference to identify one typical person with higher accuracy In this paper, the proposed method of iris recognition showed that using the iris structure for human recognition is useful and needs to develop in the future In this research, the feature extraction for recognition was worked out using the DWT based on the DIDO model and its simulation results showed the effectiveness Conclusions In this research, an iris recognition system was applied to analyze the iris features of human for recognizing a typical person In particular, the DIDO model was used to extract iris image from eye image for feature extraction and the DWT algorithm was employed to determine the iris features for identification The threshold method was utilized to exactly determine the iris feature for finding the corresponding person Simulation results are that one T threshold suitably chosen allows to determine difference between two right and left irises of one person, as well as between two irises of two persons These results mean that the 50 proposed method is the effectiveness and the basic step for development of iris identification with more optimal methods Acknowledgment The authors would like to thank Faculty of Electrical-Electronics, HCMC University of Technology and Education and the organization of Biometrics Ideal Test (CASIA-iris) Moreover, we would like to thank students and colleagues for this research Conflicts of Interest The authors declare that they have no conflict of interest References M Choraś, "Perspective methods of biometric human identification," the SPA conference, pp 195-200, 2008 R G Cruz et al., "Iris Recognition using Daugman algorithm on Raspberry Pi," the IEEE Conference (TENCON), pp 2126-2129, 2016 M Pradhan, "Next Generation Secure Computing: Biometric in Secure E-transaction," Inter Journal of Advance Research on Computer Science and Management Studies, Vol 3, No 4, pp 473-489, 2015 M Choras, "Emerging Methods of Biometrics Human 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on Haar wavelet transform,” International Journal of Security and its Applications, 8(4):265-272, 2014 51 ... nhƣng nhận diện mống mắt phức tạp hơn, có nhiều công đoạn cần thực so với nhận diện dấu vân tay So 11 với nhận diện dấu vân tay nhận diện mống mắt cần phải tách ảnh mống mắt riêng khỏi ảnh mắt. .. liệu hình ảnh Iris CASIA bao gồm 756 ảnh mống mắt từ 108 mắt, có ảnh mắt trái ảnh mắt phải ngƣời 2.3 Tiền xử lý Trƣớc thực phƣơng pháp tìm kiếm mống mắt ảnh, ảnh đƣợc thực tiền xử lý nhƣ làm... chuẩn hóa mống mắt Các bƣớc thực nhận diện mống mắt đƣợc tóm tắt nhƣ sau: Xây dựng liệu ảnh: việc thu thập hình ảnh mống mắt, bƣớc q trình nhận diện Nó thách thức lớn cho việc nhận diện mống mắt tự