COMPARISON OF SIFT AND ORB METHODS IN IDENTIFYING THE FACE OF BUDDHA STATUE

The statue is part of the heritage facial recognition process which is immobile and artistically stylized. Identifying the similarities between the statues can help provide an important reference for tourism in recognizing the faces of the statues which are different and have almost the same characteristics in every country, especially in Indonesia, among the facial recognition of the statues based on the condition, color, and shape of the face. The purpose of this study is to apply the original images that have characteristics, partially done manually to various types of transformations and calculate matching evaluation parameters such as the number of key points in the image, the level of matching, and the required execution time for each algorithm. To confirm the efficiency of the proposed method, experiments were carried out on private data sets obtained from statues under low light conditions and in different poses. The data was taken based on the image of the Buddha's face and matched with the facial image of the Buddha statue available in the database using comparisons resulting from data processing using the Sift and ORB methods with various types of transformations. The result will be seen in the image that is matched with the best algorithm for each type of distortion. The faces tested are images of the faces of the Buddha statues that are recognized, and photos of some of the original statues that were not saved due to unclear lighting and camera distance factors. The results show that the number of key points generated is the number of key points, the ORB method gives fewer results compared to the SIFT method and the average SIFT recognition and processing time shows better performance for an average of 100% at a SIFT matching rate of 2% with time 0.400285 and the ORB method is 1% for the time 0.40096

Deep Learning Architectures for Heterogeneous Face Recognition

Face recognition has been one of the most challenging areas of research in biometrics and computer vision. Many face recognition algorithms are designed to address illumination and pose problems for visible face images. In recent years, there has been significant amount of research in Heterogeneous Face Recognition (HFR). The large modality gap between faces captured in different spectrum as well as lack of training data makes heterogeneous face recognition (HFR) quite a challenging problem. In this work, we present different deep learning frameworks to address the problem of matching non-visible face photos against a gallery of visible faces. Algorithms for thermal-to-visible face recognition can be categorized as cross-spectrum feature-based methods, or cross-spectrum image synthesis methods. In cross-spectrum feature-based face recognition a thermal probe is matched against a gallery of visible faces corresponding to the real-world scenario, in a feature subspace. The second category synthesizes a visible-like image from a thermal image which can then be used by any commercial visible spectrum face recognition system. These methods also beneficial in the sense that the synthesized visible face image can be directly utilized by existing face recognition systems which operate only on the visible face imagery. Therefore, using this approach one can leverage the existing commercial-off-the-shelf (COTS) and government-off-the-shelf (GOTS) solutions. In addition, the synthesized images can be used by human examiners for different purposes. There are some informative traits, such as age, gender, ethnicity, race, and hair color, which are not distinctive enough for the sake of recognition, but still can act as complementary information to other primary information, such as face and fingerprint. These traits, which are known as soft biometrics, can improve recognition algorithms while they are much cheaper and faster to acquire. They can be directly used in a unimodal system for some applications. Usually, soft biometric traits have been utilized jointly with hard biometrics (face photo) for different tasks in the sense that they are considered to be available both during the training and testing phases. In our approaches we look at this problem in a different way. We consider the case when soft biometric information does not exist during the testing phase, and our method can predict them directly in a multi-tasking paradigm. There are situations in which training data might come equipped with additional information that can be modeled as an auxiliary view of the data, and that unfortunately is not available during testing. This is the LUPI scenario. We introduce a novel framework based on deep learning techniques that leverages the auxiliary view to improve the performance of recognition system. We do so by introducing a formulation that is general, in the sense that can be used with any visual classifier. Every use of auxiliary information has been validated extensively using publicly available benchmark datasets, and several new state-of-the-art accuracy performance values have been set. Examples of application domains include visual object recognition from RGB images and from depth data, handwritten digit recognition, and gesture recognition from video. We also design a novel aggregation framework which optimizes the landmark locations directly using only one image without requiring any extra prior which leads to robust alignment given arbitrary face deformations. Three different approaches are employed to generate the manipulated faces and two of them perform the manipulation via the adversarial attacks to fool a face recognizer. This step can decouple from our framework and potentially used to enhance other landmark detectors. Aggregation of the manipulated faces in different branches of proposed method leads to robust landmark detection. Finally we focus on the generative adversarial networks which is a very powerful tool in synthesizing a visible-like images from the non-visible images. The main goal of a generative model is to approximate the true data distribution which is not known. In general, the choice for modeling the density function is challenging. Explicit models have the advantage of explicitly calculating the probability densities. There are two well-known implicit approaches, namely the Generative Adversarial Network (GAN) and Variational AutoEncoder (VAE) which try to model the data distribution implicitly. The VAEs try to maximize the data likelihood lower bound, while a GAN performs a minimax game between two players during its optimization. GANs overlook the explicit data density characteristics which leads to undesirable quantitative evaluations and mode collapse. This causes the generator to create similar looking images with poor diversity of samples. In the last chapter of thesis, we focus to address this issue in GANs framework

Passive Techniques for Detecting and Locating Manipulations in Digital Images

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, leída el 19-11-2020El numero de camaras digitales integradas en dispositivos moviles as como su uso en la vida cotidiana esta en continuo crecimiento. Diariamente gran cantidad de imagenes digitales, generadas o no por este tipo de dispositivos, circulan en Internet o son utilizadas como evidencias o pruebas en procesos judiciales. Como consecuencia, el analisis forense de imagenes digitales cobra importancia en multitud de situaciones de la vida real. El analisis forense de imagenes digitales se divide en dos grandes ramas: autenticidad de imagenes digitales e identificacion de la fuente de adquisicion de una imagen. La primera trata de discernir si una imagen ha sufrido algun procesamiento posterior al de su creacion, es decir, que no haya sido manipulada. La segunda pretende identificar el dispositivo que genero la imagen digital. La verificacion de la autenticidad de imagenes digitales se puedellevar a cabo mediante tecnicas activas y tecnicas pasivas de analisis forense. Las tecnicas activas se fundamentan en que las imagenes digitales cuentan con \marcas" presentes desde su creacion, de forma que cualquier tipo de alteracion que se realice con posterioridad a su generacion, modificara las mismas, y, por tanto, permitiran detectar si ha existido un posible post-proceso o manipulacion...The number of digital cameras integrated into mobile devices as well as their use in everyday life is continuously growing. Every day a large number of digital images, whether generated by this type of device or not, circulate on the Internet or are used as evidence in legal proceedings. Consequently, the forensic analysis of digital images becomes important in many real-life situations. Forensic analysis of digital images is divided into two main branches: authenticity of digital images and identi cation of the source of acquisition of an image. The first attempts to discern whether an image has undergone any processing subsequent to its creation, i.e. that it has not been manipulated. The second aims to identify the device that generated the digital image. Verification of the authenticity of digital images can be carried out using both active and passive forensic analysis techniques. The active techniques are based on the fact that the digital images have "marks"present since their creation so that any type of alteration made after their generation will modify them, and therefore will allow detection if there has been any possible post-processing or manipulation. On the other hand, passive techniques perform the analysis of authenticity by extracting characteristics from the image...Fac. de InformáticaTRUEunpu

Human inspired pattern recognition via local invariant features

Vision is increasingly becoming a vital element in the manufacturing industry. As complex as it already is, vision is becoming even more difficult to implement in a pattern recognition environment as it converges toward the level of what humans visualize. Relevant brain work technologies are allowing vision systems to add capability and tasks that were long reserved for humans. The ability to recognize patterns like humans do is a good goal in terms of performance metrics for manufacturing activities. To achieve this goal, we created a neural network that achieves pattern recognition analogous to the human visual cortex using high quality keypoints by optimizing the scale space and pairing keypoints with edges as input into the model. This research uses the Taguchi Design of Experiments approach to find optimal values for the SIFT parameters with respect to finding correct matches between images that vary in rotation and scale. The approach used the Taguchi L18 matrix to determine the optimal parameter set. The performance obtained from SIFT using the optimal solution was compared with the performance from the original SIFT algorithm parameters. It is shown that correct matches between an original image and a scaled, rotated, or scaled and rotated version of that image improves by 17% using the optimal values of the SIFT. A human inspired approach was used to create a CMAC based neural network capable of pattern recognition. A comparison of 3 object, 30 object, and 50 object scenes were examined using edge and optimized SIFT based features as inputs and produced extensible results from 3 to 50 objects based on classification performance. The classification results prove that we achieve a high level of pattern recognition that ranged from 96.1% to 100% for objects under consideration. The result is a pattern recognition model capable of locally based classification based on invariant information without the need for sets of information that include input sensory data that is not necessarily invariant (background data, raw pixel data, viewpoint angles) that global models rely on in pattern recognition