Design and Analysis of A New Illumination Invariant Human Face Recognition System

In this dissertation we propose the design and analysis of a new illumination invariant face recognition system. We show that the multiscale analysis of facial structure and features of face images leads to superior recognition rates for images under varying illumination. We assume that an image I ( x,y ) is a black box consisting of a combination of illumination and reflectance. A new approximation is proposed to enhance the illumination removal phase. As illumination resides in the low-frequency part of images, a high-performance multiresolution transformation is employed to accurately separate the frequency contents of input images. The procedure is followed by a fine-tuning process. After extracting a mask, feature vector is formed and the principal component analysis (PCA) is used for dimensionality reduction which is then proceeded by the extreme learning machine (ELM) as a classifier. We then analyze the effect of the frequency selectivity of subbands of the transformation on the performance of the proposed face recognition system. In fact, we first propose a method to tune the characteristics of a multiresolution transformation, and then analyze how these specifications may affect the recognition rate. In addition, we show that the proposed face recognition system can be further improved in terms of the computational time and accuracy. The motivation for this progress is related to the fact that although illumination mostly lies in the low-frequency part of images, these low-frequency components may have low- or high-resonance nature. Therefore, for the first time, we introduce the resonance based analysis of face images rather than the traditional frequency domain approaches. We found that energy selectivity of the subbands of the resonance based decomposition can lead to superior results with less computational complexity. The method is free of any prior information about the face shape. It is systematic and can be applied separately on each image. Several experiments are performed employing the well known databases such as the Yale B, Extended-Yale B, CMU-PIE, FERET, AT&T, and LFW. Illustrative examples are given and the results confirm the effectiveness of the method compared to the current results in the literature

Human face recognition under degraded conditions

Comparative studies on the state of the art feature extraction and classification techniques for human face recognition under low resolution problem, are proposed in this work. Also, the effect of applying resolution enhancement, using interpolation techniques, is evaluated. A gradient-based illumination insensitive preprocessing technique is proposed using the ratio between the gradient magnitude and the current intensity level of image which is insensitive against severe level of lighting effect. Also, a combination of multi-scale Weber analysis and enhanced DD-DT-CWT is demonstrated to have a noticeable stability versus illumination variation. Moreover, utilization of the illumination insensitive image descriptors on the preprocessed image leads to further robustness against lighting effect. The proposed block-based face analysis decreases the effect of occlusion by devoting different weights to the image subblocks, according to their discrimination power, in the score or decision level fusion. In addition, a hierarchical structure of global and block-based techniques is proposed to improve the recognition accuracy when different image degraded conditions occur. Complementary performance of global and local techniques leads to considerable improvement in the face recognition accuracy. Effectiveness of the proposed algorithms are evaluated on Extended Yale B, AR, CMU Multi-PIE, LFW, FERET and FRGC databases with large number of images under different degradation conditions. The experimental results show an improved performance under poor illumination, facial expression and, occluded images

PENDEKATAN ROBUST REGRESSION UNTUK PENGENALAN WAJAH DENGAN HISTOGRAM REMAPPING DISTRIBUSI NORMAL PADA TAHAP PRA-PEMROSESAN MENGGUNAKAN CMU-PIE FACE DATABASE

Dalam paradigma pengenalan wajah, variasi iluminasi dianggap sebagai salah satu masalah utama terkait dengan kehandalan (robustness). Banyak pendekatan yang dikembangkan untuk memecahkan masalah ini, salah satunya adalah Robust Regression, yang menunjukkan hasil lebih baik dibandingkan dengan sejumlah pendekatan lainnya. Pada tahap pra pemrosesan, pendekatan ini menggunakan histogram equalization (histogram remapping dengan distribusi uniform), sebagaimana umum digunakan pada banyak pendekatan pengenalan wajah. Terkait dengan histogram equalization, dalam sebuah penelitian lain yang menguji berbagai teknik Histogram Remapping pada pendekatan LDA (Linear Discriminant Analysis), disimpulkan bahwa penggunaan histogram remapping dengan distribusi non-uniform lebih sesuai untuk pengenalan wajah dibandingkan dengan histogram equalization. Oleh karena itu, pada penelitian ini diusulkan penggunaan histogram remapping dengan salah satu jenis distribusi non-uniform, yaitu distribusi normal, yang menggantikan histogram equalization pada tahap pra pemrosesan dalam pendekatan Robust Regression. Hasil uji coba yang dilakukan pada CMU-PIE Face Database menunjukkan bahwa penggunaan histogram remapping dengan distribusi normal pada metode yang diusulkan menghasilkan tingkat akurasi lebih tinggi daripada metode sebelumnya. Penelitian lebih lanjut dapat dilakukan dengan melakukan uji coba pada basisdata citra lain sebagai pembanding hasil penelitian ini. Selain itu, bisa diteliti lebih lanjut juga mengenai penggunaan histogram remapping dengan distribusi non-uniform lain pada pendekatan Robust Regression, sehingga nantinya dapat diketahui distribusi non-uniform apa saja yang dapat digunakan dalam histogram remapping untuk menghasilkan kinerja pendekatan Robust Regression pada pengenalan wajah yang lebih baik. Kata Kunci: Pengenalan Wajah, Normalisasi Iluminasi, Robust Regression, Histogram Equalization, Histogram Remapping, Disribusi Normal

PENGENALAN CITRA WAJAH DENGAN VARIASI ILUMINASI MENGGUNAKAN PRA-PEMROSESAN TAN AND TRIGGS DAN METODE KLASIFIKASI ROBUST REGRESSION

Abstrak. Pengenalan citra wajah dengan variasi iluminasi dianggap sebagai salah satu masalah penting di bidang pengenalan wajah karena variasi yang disebabkan oleh pencahyaan lebih signifikan dari pada ciri fisik wajah individu sendiri. Salah satu pendekatan untuk memecahkan masalah ini  adalah dengan metode klasifikasi Robust Regression. Dalam penelitian ini metode Robust Regression dengan menggunakan teknik pra pemrosesan Tan and Triggs (TT)  dapat menghasilkan kinerja yang cukup handal. Pengujian dilakukan dengan menggunakan 2 basisdata standar yaitu CMU-PIE dan Yale Face B. Berdasarkan uji coba yang dilakukan, penggunaan pra pemrosesan TT pada robust regression menghasilkan tingakat akurasi yang lebih unggul daripada penggunaan pra pemrosesan Histogram Equalization (HE). Pada CMU PIE Face Database pencahayaan frontal dengan pra proses  HE akurasi sebesar 97,30% sedangkan dengan TT akurasi sebesar 97,82%. Pada kondisi pencahayaan ekstrim akurasi yang diperoleh HE sebesar 99,66% sedangkan TT sebesar 100%. Selain itu, dari hasil uji coba database lain yaitu dengan Yale Face Database B 50x50 akurasi menggunakan HE sebesar 84,7 % sedangkan dengan TT sebesar 93,95%.   Kata Kunci: Pengenalan Wajah, Normalisasi Iluminasi, Robust Regression, Tan and Triggs

PENGENALAN WAJAH DENGAN PENDEKATAN ROBUST REGRESSION YANG MENGGUNAKAN HISTOGRAM REMAPPING DENGAN DISTRIBUSI NON-UNIFORM

Dalam paradigma pengenalan wajah, variasi iluminasi (variasi sumber pencahayaan yang menghasilkan perbedaan citra) dianggap sebagai salah satu masalah utama terkait dengan kehandalan (robustness). Banyak pendekatan yang dikembangkan untuk memecahkan masalah ini. Salah satunya adalah pendekatan Robust Regression, yang menunjukkan hasil lebih baik dibandingkan dengan sejumlah pendekatan lainnya. Pada tahap pra pemrosesan, pendekatan Robust Regression menggunakan Histogram Equalization (Histogram Remapping dengan distribusi uniform). Dalam penelitian lain, yang menguji berbagai teknik Histogram Remapping dengan pendekatan LDA (Linear Discriminant Analysis), disimpulkan bahwa penggunaan Histogram Remapping dengan distribusi non-uniform lebih sesuai untuk pengenalan wajah dibandingkan dengan Histogram Equalization. Oleh karena itu, pada penelitian ini diusulkan penggunaan Histogram Remapping dengan distribusi non-uniform, yang menggantikan Histogram Equalization, pada tahap pra pemrosesan dalam pendekatan Robust Regression. Berdasarkan uji coba dan analisis yang dilakukan, penggunaan histogram remapping dengan distribusi non-uniform pada robust regression menghasilkan tingkat akurasi yang lebih daripada metode sebelumnya. Secara khusus, dari ketiga distribusi yang digunakan (normal, lognormal, dan eksponensial), hanya distribusi normal yang memberikan tingkat akurasi lebih baik dari metode sebelumnya. Untuk lebih meningkatkan kinerja pengenalan wajah, penelitian lebih lanjut dapat dilakukan untuk menguji jenis distribusi lain pada histogram remapping dalam pendekatan robust regression. Kata Kunci: Pengenalan Wajah, Normalisasi Iluminasi, Robust Regression, Histogram Equalization, Distribusi Non-Uniform

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Features extraction for low-power face verification

Mobile communication devices now available on the market, such as so-called smartphones, are far more advanced than the first cellular phones that became very popular one decade ago. In addition to their historical purpose, namely enabling wireless vocal communications to be established nearly everywhere, they now provide most of the functionalities offered by computers. As such, they hold an ever-increasing amount of personal information and confidential data. However, the authentication method employed to prevent unauthorized access to the device is still based on the same PIN code mechanism, which is often set to an easy-to-guess combination of digits, or even altogether disabled. Stronger security can be achieved by resorting to biometrics, which verifies the identity of a person based on intrinsic physical or behavioral characteristics. Since most mobile phones are now equipped with an image sensor to provide digital camera functionality, biometric authentication based on the face modality is very interesting as it does not require a dedicated sensor, unlike e.g. fingerprint verification. Its perceived intrusiveness is furthermore very low, and it is generally well accepted by users. The deployment of face verification on mobile devices however requires overcoming two major challenges, which are the main issues addressed in this PhD thesis. Firstly, images acquired by a handheld device in an uncontrolled environment exhibit strong variations in illumination conditions. The extracted features on which biometric identification is based must therefore be robust to such perturbations. Secondly, the amount of energy available on battery-powered mobile devices is tightly constrained, calling for algorithms with low computational complexity, and for highly optimized implementations. So as to reduce the dependency on the illumination conditions, a low-complexity normalization technique for features extraction based on mathematical morphology is introduced in this thesis, and evaluated in conjunction with the Elastic Graph Matching (EGM) algorithm. Robustness to other perturbations, such as occlusions or geometric transformations, is also assessed and several improvements are proposed. In order to minimize the power consumption, the hardware architecture of a coprocessor dedicated to features extraction is proposed and described in VHDL. This component is designed to be integrated into a System-on-Chip (SoC) implementing the complete face verification process, including image acquisition, thereby enabling biometric face authentication to be performed entirely on the mobile device. Comparison of the proposed solution with state-of-the-art academic results and recently disclosed commercial products shows that the chosen approach is indeed much more efficient energy-wise

Development of Multirate Filter – Based Region Features for Iris Identification

The emergence of biometric system is seen as the next-generation technological solution in strengthening the social and national security. The evolution of biometrics has shifted the paradigm of authentication from classical token and knowledge-based systems to physiological and behavioral trait based systems. R & D on iris biometrics, in last one decade, has established it as one of the most promising traits. Even though, iris biometric takes high resolution near-infrared (NIR) images as input, its authentication accuracy is very commendable. Its performance is often influenced by the presence of noise, database size, and feature representation. This thesis focuses on the use of multi resolution analysis (MRA) in developing suitable features for non-ideal iris images. Our investigation starts with the iris feature extraction technique using Cohen −Daubechies − Feauveau 9/7 (CDF 9/7) filter bank. In this work, a technique has been proposed to deal with issues like segmentation failure and occlusion. The experimental studies deal with the superiority of CDF 9/7 filter bank over the frequency based techniques. Since there is scope for improving the frequency selectivity of CDF 9/7 filter bank, a tunable filter bank is proposed to extract region based features from non-cooperative iris images. The proposed method is based on half band polynomial of 14th order. Since, regularity and frequency selectivity are in inverse relationship with each other, filter coefficients are derived by not imposing maximum number of zeros. Also, the half band polynomial is presented in x-domain, so as to apply semidefinite programming, which results in optimization of coefficients of analysis/synthesis filter. The next contribution in this thesis deals with the development of another powerful MRA known as triplet half band filter bank (THFB). The advantage of THFB is the flexibility in choosing the frequency response that allows one to overcome the magnitude constraints. The proposed filter bank has improved frequency selectivity along with other desired properties, which is then used for iris feature extraction. The last contribution of the thesis describes a wavelet cepstral feature derived from CDF 9/7 filter bank to characterize iris texture. Wavelet cepstrum feature helps in reducing the dimensionality of the detail coefficients; hence, a compact feature presentation is possible with improved accuracy against CDF 9/7. The efficacy of the features suggested are validated for iris recognition on three publicly available databases namely, CASIAv3, UBIRISv1, and IITD. The features are compared with other transform domain features like FFT, Gabor filter and a comprehensive evaluation is done for all suggested features as well. It has been observed that the suggested features show superior performance with respect to accuracy. Among all suggested features, THFB has shown best performance

Deep learning in food category recognition

Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent of big data and the development of data-oriented fields like deep learning have provided advancements in food category recognition. With increasing computational power and ever-larger food datasets, the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We survey the core components for constructing a machine learning system for food category recognition, including datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial Fund (RP202G0289)LIAS (P202ED10Data Science Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK Education Fund (OP202006)BBSRC (RM32G0178B8