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This paper presents a face recognition approach integrated in a simplified system used for profile administration of patients and prosopagnosia applications. The recognition system is based on three successive filters to properly retrieve frontal faces in various illumination conditions and pose variations, a single multi-layer perceptron (MLP) classifier per user and a global comparator of features...
Parking and maneuvering accidents are responsible for a significant amount of real-world — especially property damage — accidents. Insurance research estimates up to 40 % of all claims and up to 30 % of all claim-associated costs around the world are caused by these type of accidents. Therefore, a 360° low speed autonomous emergency braking system could have a high monetary effectiveness. To design...
This paper presents an approach called Gabor-feature-based Local Generic Representation (G-LGR), which take advantages of the sparse representation properties of face recognition in biometric applications. In this work, the main problem is that if only one training subject per class is available. One of the novelties of our new algorithm is to produce virtual samples of each subject; the new sample...
We propose a vision-based method that localizes a ground vehicle using publicly available satellite imagery as the only prior knowledge of the environment. Our approach takes as input a sequence of ground-level images acquired by the vehicle as it navigates, and outputs an estimate of the vehicle's pose relative to a georeferenced satellite image. We overcome the significant viewpoint and appearance...
We optimize and compare the performance of different deep learning architectures for awareness on a sidewalk using small form factor devices such as Raspberry Pi 3. Our main objective is to find deep learning architecture that is complex enough to accurately classify a set obstacles on the sidewalk. Out selection criteria are: minimum number of parameters, lower power consumption, and robustness against...
Face recognition in real scenarios is mainly affected by illumination variation and occlusion, and therefore in order to develop a robust face recognition system these issues should be handled simultaneously. To this aim, the steps involved in the presented framework are (i) computationally simple and efficient preprocessing chain that eliminates major effects of illumination variation and noise while...
Recently, deep features extracted from Convolutional Neural Networks (CNNs) have been widely adopted in various applications, such as face recognition. Compared with the handcrafted descriptors, deep features have more powerful representation ability which can lead to better performance. Effective feature representations play an important role in ear recognition. While deep features have not been...
Nowadays research has explored to extracting auxiliary information from various biometrie techniques such as fingerprints, face, iris, palm, voice etc. This information contains some features like gender, age, beard, mustache, scars, height, hair, skin color, glasses, weight, facial marks, tattoos etc. All this information contributes more and more to identification. The major changes that come across...
In this paper we describe a unique video database which consists of the real life moments of people and objects, captured under various illumination conditions and camera positions. We have classified all the videos of our database into six categories, out of which four categories are based on the movements of camera and objects (captured by the camera). The remaining categories of the database are...
In this paper, we consider the robust face recognition problem via iterative re-constrained group sparse classifier (IRGSC) with adaptive weights learning. Specifically, we propose a group sparse representation classification (GSRC) approach in which weighted features and groups are collaboratively adopted to encode more structure information and discriminative information than other regression based...
In this paper, we have implemented and tested a system of detection and recognition of road signs. The approach taken in this work consists of two main modules: a sensor module, which is based on color segmentation and shape detection where we converted the images to the HSV color space, then labeled the detected regions and tested for their shape. A recognition module, Template Matching, whose role...
Planar spoofing is a well researched problem, wherein a high quality planar photograph can be replayed in front of a still camera as a substitute for another individual's face. Most modern day face recognition systems can be fooled by this process, as the perceptual information contained in a photo-of-a-photo, is virtually the same as that of a natural photograph of an individual. Current solutions...
In this paper, we propose a patch based semi-supervised linear regression (PSLR) approach to address single sample per person (SSPP) problem in face recognition, which takes full use of the unlabeled probe samples to learn facial variation information. We partition each face image into several overlapped patches, where each patch corresponds to a mapping matrix of regression model. Then, mapping matrix...
This paper presents a face recognition algorithm based on Local Binary Pattern (LBP) to be implemented in a Smartphone with Android operating system where the input image is obtained using the camera of such Smartphone. The LBP algorithm is used for Face characterization, due to its low complexity and its robustness light of this method is chosen to be applied in a Smartphone, this is because the...
Face recognition is a fascinating research area which has potential applications in almost every field in this world. Having said this, there is a strong need for developing a stubborn system which can overcome all problems faced in recognizing a face correctly. The flow of face recognition system is preprocessing, Feature extraction and classification. Pre-processing includes image resizing, image...
Compared to the traditional Gabor transform, the circularly symmetrical Gabor transform (CSGT) not only retains the characteristics of local and multi-resolution analysis, but also has the remarkable advantages of less redundancy and rotational invariance. Simultaneously, the collaborative representation-based classification with regularized least square (CRC-RLS) overcomes the shortcoming of the...
Feature refers to some relevant information which is present on images or faces. Feature extraction used to extract those features from the face. Among that bulk of keypoints, only robust features are detected by using feature descriptors. This paper analyzes 2 robust feature detector and descriptors are: Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF). These two robust...
This paper proposes a novel facial image representation Block-based Local Contrast Patterns (BLCP) for illumination-robust face recognition. This method is based on an effective texture descriptor local contrast patterns (LCP). We use the directed and undirected difference masks to calculate three types of local intensity contrasts: directed, undirected, and maximum difference responses. These response...
The performance of local descriptors such as SIFT drops under severe illumination changes. In this paper, we propose a Discriminative and Contrast Invertible (DCI) local feature descriptor. In order to increase the discriminative ability of the descriptor under illumination changes, a Laplace gradient based histogram is proposed. Moreover, a robust contrast flipping estimate is proposed based on the...
In recent years, face recognition has become a popular topic in academia and industry. Current local methods such as the local binary pattern (LBP), and scale invariant feature transform (SIFT) perform better than holistic methods, but their high complexity levels limit their application. In addition, SIFT-based schemes are sensitive to illumination variation. We propose an LBP edge-mapped descriptor...
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