Three-dimensional face recognition: An Eigensurface approach

We evaluate a new approach to face recognition using a variety of surface representations of three-dimensional facial structure. Applying principal component analysis (PCA), we show that high levels of recognition accuracy can be achieved on a large database of 3D face models, captured under conditions that present typical difficulties to more conventional two-dimensional approaches. Applying a ran-c of image processing, techniques we identify the most effective surface representation for use in such application areas as security surveillance, data compression and archive searching

PENGENALAN WAJAH SECARA REAL TIME DENGAN SMARTPHONE ANDROID

Manusia memiliki kemampuan untuk mengenali wajah dengan akurasi minimal 90% bahkan ketika tidak bertemu dengan wajah tersebut selama puluhan tahun, namun kemampuan seseorang untuk mengingat atau mencocokan wajah tersebut agak kurang. Terkadang pada saat menjumpai seseorang yang ada di album foto, seringkali merasakan sepertinya sudah pernah mengenali atau familiar dengan orang tersebut tetapi tidak mengingat bahkan lupa identitas siapakah orang tersebut. Oleh karena itu diperlukan suatu sistem untuk mengenali wajah seseorang yang dapat digunakan sebagai pengingat apakah seseorang yang dijumpai tersebut sudah pernah dikenali sebelumnya atau belum. Pada tulisan ini, penulis melakukan pengenalan wajah yang menggunakan metode eigenface sebagai metode yang digunakan secara real time. Pada pembuatan sistem ini, penulis menggunakan bahasa pemrograman java dan sistem operasi android sebagai platformnya. Tujuan dalam penulisan ini adalah dapat mengembangkan aplikasi pengenalan wajah berbasis mobile dengan tingkat akurasi dan kecepatan yang lebih baik secara real time. Pada tahap implementasi yang dilakukan, menghasilkan hasil pengenalan yang terbaik yaitu dengan dtingkat akurasi sebesar 66,67% dengan rata- rata waktu pengenalannya adalah 333,33 ms dengan kondisi pencahayaan yang mendukung

Automatic face recognition using stereo images

Face recognition is an important pattern recognition problem, in the study of both natural and artificial learning problems. Compaxed to other biometrics, it is non-intrusive, non- invasive and requires no paxticipation from the subjects. As a result, it has many applications varying from human-computer-interaction to access control and law-enforcement to crowd surveillance. In typical optical image based face recognition systems, the systematic vaxiability arising from representing the three-dimensional (3D) shape of a face by a two-dimensional (21)) illumination intensity matrix is treated as random vaxiability. Multiple examples of the face displaying vaxying pose and expressions axe captured in different imaging conditions. The imaging environment, pose and expressions are strictly controlled and the images undergo rigorous normalisation and pre-processing. This may be implemented in a paxtially or a fully automated system. Although these systems report high classification accuracies (>90%), they lack versatility and tend to fail when deployed outside laboratory conditions. Recently, more sophisticated 3D face recognition systems haxnessing the depth information have emerged. These systems usually employ specialist equipment such as laser scanners and structured light projectors. Although more accurate than 2D optical image based recognition, these systems are equally difficult to implement in a non-co-operative environment. Existing face recognition systems, both 2D and 3D, detract from the main advantages of face recognition and fail to fully exploit its non-intrusive capacity. This is either because they rely too much on subject co-operation, which is not always available, or because they cannot cope with noisy data. The main objective of this work was to investigate the role of depth information in face recognition in a noisy environment. A stereo-based system, inspired by the human binocular vision, was devised using a pair of manually calibrated digital off-the-shelf cameras in a stereo setup to compute depth information. Depth values extracted from 2D intensity images using stereoscopy are extremely noisy, and as a result this approach for face recognition is rare. This was cofirmed by the results of our experimental work. Noise in the set of correspondences, camera calibration and triangulation led to inaccurate depth reconstruction, which in turn led to poor classifier accuracy for both 3D surface matching and 211) 2 depth maps. Recognition experiments axe performed on the Sheffield Dataset, consisting 692 images of 22 individuals with varying pose, illumination and expressions

Robust Recognition using L1-Principal Component Analysis

The wide availability of visual data via social media and the internet, coupled with the demands of the security community have led to an increased interest in visual recognition. Recent research has focused on improving the accuracy of recognition techniques in environments where variability is well controlled. However, applications such as identity verification often operate in unconstrained environments. Therefore there is a need for more robust recognition techniques that can operate on data with considerable noise. Many statistical recognition techniques rely on principal component analysis (PCA). However, PCA suffers from the presence of outliers due to occlusions and noise often encountered in unconstrained settings. In this thesis we address this problem by using L1-PCA to minimize the effect of outliers in data. L1-PCA is applied to several statistical recognition techniques including eigenfaces and Grassmannian learning. Several popular face databases are used to show that L1-Grassmann manifolds not only outperform, but are also more robust to noise and occlusions than traditional L2-Grassmann manifolds for face and facial expression recognition. Additionally a high performance GPU implementation of L1-PCA is developed using CUDA that is several times faster than CPU implementations

Towards Practical Face Recognition System Employing Row-Based Distance Method In 2dpca Based Algorithms

Automatic face recognition has been a focus research topic in past few decades. This is due to the advantages of face recognition and the potential need for high security in commercial and law enforcement applications. However, due to nature of the face, it is subjected to several variations. Thus, finding a good face recognition system is still an active research field till today. Many approaches have been proposed to overcome the face variations. In the midst of these techniques, subspace methods are considered the most popular and powerful techniques. Among them, eigenface or Principal Component Analysis (PCA) method is considered as one of the most successful techniques in subspace methods. One of the most important extensions of PCA is Two-dimensional PCA (2DPCA). However, 2DPCA-based features are matrices rather than vectors as in PCA. Hence, different distance computation methods have been proposed to calculate the distance between the test feature matrix and the training feature matrices. All previous methods deal with the classification problem mathematically without any consideration between feature matrices and the face images. Besides, the system performance in practical applications relies on the number of eigenvectors chosen. As a solution to the above mentioned issues, four new distance methods have been proposed in this thesis, which are based on the rows of a feature matrix of 2DPCA-based algorithms. Through experiments, using eight face databases, their improvements compared to the previous distance methods are demonstrated

Fuzzy Interval-Valued Multi Criteria Based Decision Making for Ranking Features in Multi-Modal 3D Face Recognition

Soodamani Ramalingam, 'Fuzzy interval-valued multi criteria based decision making for ranking features in multi-modal 3D face recognition', Fuzzy Sets and Systems, In Press version available online 13 June 2017. This is an Open Access paper, made available under the Creative Commons license CC BY 4.0 https://creativecommons.org/licenses/by/4.0/This paper describes an application of multi-criteria decision making (MCDM) for multi-modal fusion of features in a 3D face recognition system. A decision making process is outlined that is based on the performance of multi-modal features in a face recognition task involving a set of 3D face databases. In particular, the fuzzy interval valued MCDM technique called TOPSIS is applied for ranking and deciding on the best choice of multi-modal features at the decision stage. It provides a formal mechanism of benchmarking their performances against a set of criteria. The technique demonstrates its ability in scaling up the multi-modal features.Peer reviewedProo