RGB-D FACE RECOGNITION USING LBP-DCT ALGORITHM

Face recognition is one of the applications in image processing that recognizes or checks an individual's identity. 2D images are used to identify the face, but the problem is that this kind of image is very sensitive to changes in lighting and various angles of view. The images captured by 3D camera and stereo camera can also be used for recognition, but fairly long processing times is needed. RGB-D images that Kinect produces are used as a new alternative approach to 3D images. Such cameras cost less and can be used in any situation and any environment. This paper shows the face recognition algorithms’ performance using RGB-D images. These algorithms calculate the descriptor which uses RGB and Depth map faces based on local binary pattern. Those images are also tested for the fusion of LBP and DCT methods. The fusion of LBP and DCT approach produces a recognition rate of 97.5% during the experiment

Learned Spatio-Temporal Texture Descriptors for RGB-D Human Action Recognition

Due to the recent arrival of Kinect, action recognition with depth images has attracted researchers' wide attentions and various descriptors have been proposed, where Local Binary Patterns (LBP) texture descriptors possess the properties of appearance invariance. However, the LBP and its variants are most artificially-designed, demanding engineers' strong prior knowledge and not discriminative enough for recognition tasks. To this end, this paper develops compact spatio-temporal texture descriptors, i.e. 3D-compact LBP (3D-CLBP) and local depth patterns (3D-CLDP), for color and depth videos in the light of compact binary face descriptor learning in face recognition. Extensive experiments performed on three standard datasets, 3D Online Action, MSR Action Pairs and MSR Daily Activity 3D, demonstrate that our method is superior to most comparative methods in respects of performance and can capture spatial-temporal texture cues in videos

Gender recognition from facial images: Two or three dimensions?

© 2016 Optical Society of America. This paper seeks to compare encoded features from both two-dimensional (2D) and three-dimensional (3D) face images in order to achieve automatic gender recognition with high accuracy and robustness. The Fisher vector encoding method is employed to produce 2D, 3D, and fused features with escalated discriminative power. For 3D face analysis, a two-source photometric stereo (PS) method is introduced that enables 3D surface reconstructions with accurate details as well as desirable efficiency. Moreover, a 2D + 3D imaging device, taking the two-source PS method as its core, has been developed that can simultaneously gather color images for 2D evaluations and PS images for 3D analysis. This system inherits the superior reconstruction accuracy from the standard (three or more light) PS method but simplifies the reconstruction algorithm as well as the hardware design by only requiring two light sources. It also offers great potential for facilitating human computer interaction by being accurate, cheap, efficient, and nonintrusive. Ten types of low-level 2D and 3D features have been experimented with and encoded for Fisher vector gender recognition. Evaluations of the Fisher vector encoding method have been performed on the FERET database, Color FERET database, LFW database, and FRGCv2 database, yielding 97.7%, 98.0%, 92.5%, and 96.7% accuracy, respectively. In addition, the comparison of 2D and 3D features has been drawn from a self-collected dataset, which is constructed with the aid of the 2D + 3D imaging device in a series of data capture experiments. With a variety of experiments and evaluations, it can be proved that the Fisher vector encoding method outperforms most state-of-the-art gender recognition methods. It has also been observed that 3D features reconstructed by the two-source PS method are able to further boost the Fisher vector gender recognition performance, i.e., up to a 6% increase on the self-collected database

Face recognition with the RGB-D sensor

Face recognition in unconstrained environments is still a challenge, because of the many variations of the facial appearance due to changes in head pose, lighting conditions, facial expression, age, etc. This work addresses the problem of face recognition in the presence of 2D facial appearance variations caused by 3D head rotations. It explores the advantages of the recently developed consumer-level RGB-D cameras (e.g. Kinect). These cameras provide color and depth images at the same rate. They are affordable and easy to use, but the depth images are noisy and in low resolution, unlike laser scanned depth images. The proposed approach to face recognition is able to deal with large head pose variations using RGB-D face images. The method uses the depth information to correct the pose of the face. It does not need to learn a generic face model or make complex 3D-2D registrations. It is simple and fast, yet able to deal with large pose variations and perform pose-invariant face recognition. Experiments on a public database show that the presented approach is effective and efficient under significant pose changes. Also, the idea is used to develop a face recognition software that is able to achieve real-time face recognition in the presence of large yaw rotations using the Kinect sensor. It is shown in real-time how this method improves recognition accuracy and confidence level. This study demonstrates that RGB-D sensors are a promising tool that can lead to the development of robust pose-invariant face recognition systems under large pose variations

Face Image Modality Recognition and Photo-Sketch Matching

Face is an important physical characteristic of human body, and is widely used in many crucial applications, such as video surveillance, criminal investigation, and security access system. Based on realistic demand, such as useful face images in dark environment and criminal profile, different modalities of face images appeared, e.g. three-dimensional (3D), near infrared (NIR), and thermal infrared (TIR) face images. Thus, researches with various face image modalities become a hot area. Most of them are set on knowing the modality of face images in advance, which contains a few limitations. In this thesis, we present approaches for face image modality recognition to extend the possibility of cross-modality researches as well as handle new modality-mixed face images. Furthermore, a large facial image database is assembled with five commonly used modalities such as 3D, NIR, TIR, sketch, and visible light spectrum (VIS). Based on the analysis of results, a feature descriptor based on convolutional neural network with linear kernel SVM did an optimal performance.;As we mentioned above, face images are widely used in crucial applications, and one of them is using the sketch of suspect\u27s face, which based on the witness\u27 description, to assist law enforcement. Since it is difficult to capture face photos of the suspect during a criminal activity, automatic retrieving photos based on the suspect\u27s facial sketch is used for locating potential suspects. In this thesis, we perform photo-sketch matching by synthesizing the corresponding pseudo sketch from a given photo. There are three methods applied in this thesis, which are respectively based on style transfer, DualGAN, and cycle-consistent adversarial networks. Among the results of these methods, style transfer based method did a poor performance in photo-sketch matching, since it is an unsupervised one which is not purposeful in photo to sketch synthesis problem while the others need to train pointed models in synthesis stage

An Experimental and Numerical Investigation of Nitrogen Dioxide Emissions Characteristics of Compression Ignition Dual Fuel Engines

Detailed experimental research was conducted to explore the impact of the addition of gaseous fuels, including H2 and natural gas (NG), and engine load on the emissions of NO2, NO, and NOx from dual fuel engines. The addition of less than 2% of H2 or NG was shown to dramatically increase the emissions of NO2 until a maximum level of NO2 emissions was reached. The increased NO 2 emissions were due to the conversion of NO to NO2. The maximum NO2/NOx ratio obtained with the addition of H2 was 3.2 to 5.0 times that of diesel operation. The maximum NO 2/NOx ratio obtained with the addition of NG was 3.4 to 4.3 times that of diesel operation. Further increasing the amount of gaseous fuel beyond the point of maximum NO2 emissions resulted in a reduction of NO2 emissions. Detailed examination of factors having the potential to affect the formation of NOx and NO2 in compression ignition engines reported a firm correlation between the emissions of NO 2 and emissions of unburned H2 and methane (CH4), and their relative emissions. The presence of unburned gaseous fuels that survived the main combustion process appears to be one of the main factors contributing to the enhanced conversion of NO to NO2. This was supported by the experimental data reported in the literature. The presence of fumigation fuels outside the diesel spray plume might be the main factor contributing to the increased emissions of NO2 from dual fuel engines. The spontaneous combustion of fumigation fuels that are entrained into the diesel spray plume may not contribute to the increased emissions of NO 2. In comparison, the correlations between the increased emissions of NO2 and the variation in bulk mixture temperature and heat release process including maximum heat release rate, and combustion duration were weak.;A single zone, zero-dimensional, constant volume numerical model with detailed chemistry was used to simulate the oxidization process of the gaseous fuel, as well as its effect on the conversion of NO to NO2 after the post-combustion mixing of the gaseous fuel surviving the main combustion process with the NOx-containing combustion products. The gaseous fuel examined included CH4, H2, and carbon monoxide (CO). The simulation results revealed the significant effects of the fuel mixed, its initial concentration in the mixture, and the initial temperature on the oxidization of gaseous fuel, the conversion of NO to NO2, and the destruction of NO2 to NO after the completion of the oxidation process.;The single zone zero-dimensional model was further modified to a variable volume model with the volume of the combustion chamber calculated using the geometry of the 1999 Cummins engine and engine speed. The modified variable volume model with detailed chemistry was used to improve the simulation of the effect on the conversion of NO to NO2 of the post-combustion mixing of surviving gaseous fuel with NOx-containing combustion products. The spatial variation of the local bulk mixture temperature with the progress of the combustion process and the variation of cylinder volume during the expansion process was taken into account by a pseudo temperature at the top dead center (TDC) noted as Tpseudo TDC defined in this research. The simulation identified the importance of the phasing of postcombustion mixing on the oxidation of gaseous fuel and its effect on the conversion of NO to NO2.;A preliminary sensitivity analysis was also conducted to identify the reactions having significant effect on the conversion of NO to NO2 and its destruction to NO. Among the four reactions associated with the formation and destruction of NO2, R186 was identified as the main reaction to the formation of NO2 during the oxidation process of H 2 and CO. This was due to the high concentration of HO2 formed during the oxidation process of H2 and CO in the combustion product. The destruction of NO2 to NO occurred through R187 and R189. (Abstract shortened by UMI.)