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Similarity rank lists provide a method for learning generalization of classifiers from examples. Here, we apply it to invariant object recognition and demonstrate that it performs better than other approaches on view and illumination invariant recognition. Recognition from a single view reaches 87% success rate. To study its real world capabilities we introduce subsqare rank matching that works on...
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 this paper, we present a new approach for periocular recognition based on the Symmetry Assessment by Feature Expansion (SAFE) descriptor, which encodes the presence of various symmetric curve families around image key points. We use the sclera center as single key point for feature extraction, highlighting the object-like identity properties that concentrates to this unique point of the eye. As...
In this paper, we propose a new texture descriptor, completed local derivative pattern (CLDP). In contrast to completed local binary pattern (CLBP), which involves only local differences at each scale, CLDP encodes the directional variation of the local differences of two scales as a complementary component to local patterns in CLBP. The new component in CLDP, with regarded as the directional derivative...
This paper presents a study case for color inspection in quality control applications using color descriptors histogram RGB-1D and histogram TSL and supervised machine learning methods such Support Vector Machine (SVM) and Artificial Neural Networks (ANN). For this, we build three annotated databases, and these are made using real application of quality control like color inspection in forages and...
Pedestrian detection is one of the challenging research topics in computer vision and efficient feature representation of a pedestrian attracts more and more attention. Traditional features such as Histogram of Oriented Gradients (HOG) were widely used in pedestrian detection, but because of their poor texture description ability, these feature based methods cannot achieve satisfactory pedestrian...
In the absence of lighting, due to the difference in the modality, it is very difficult to match the visible face database with the face images obtained in the thermal wavelength. Applying photometric preprocessing to reduce the modality difference, increases the face recognition performance from thermal to visible. In this study, different photometric preprocessing methods that can be used for face...
In any image, illumination is one of the challenges task and effect the performance of the system. In this paper, we have proposed new preprocessing approach to eliminate illumination effect from the human face images. In our approach we first apply Log transform on the input image to enhance illumination effect, output of this is given as input to the DoG filtering technique to smooth the image and...
The current paper suggests an initial segmentation system, detection and grouping of visual information based on faces to facilitate the use, the handling and validation of the VIDTIMIT database. In order to implement this basis, two methods of pretreatment for each face are used. Then faces are detected with the Viola and Jones algorithm based on weak type descriptors Haar and grouped by new faces...
One of the major challenges in face recognition is that related to the differences in orientation or pose, the variations of illumination, the facial expressions, the occlusions and aging. In this paper, we propose an efficient method for face recognition in an uncontrolled environment where we fuse Gabor wavelets and Local Binary Patterns (LBP) in the feature extraction phase. Then, we apply the...
Single face-image comparisons are extremely challenging, particularly in the context of pose, expression variations and scene illumination changes. Most of the existing schemes are sub-space learning based, where dominant eigen-directions are determined from the covariance matrix computed over the entire face space. In this paper we propose a simple hashing method based on the relative magnitudes...
Estimating eye centers is an important computer vision problem with several applications. In the past, eye center localization was constrained by the use of special hardware such as infrared cameras. Methods that estimate eye centers based on visible light have also been suggested in the literature, but these methods are inaccurate when used with low resolution images and wide ranges of lighting....
Quality of an image plays a fundamental role in taking vital decisions. In various walks of life, one such decision is personal identification. Hence, it's assessment is essential prior to using it in many biometric applications such as face recognition, iris, fingerprint analysis etc. The proposed technique classifies images into four classes based on their illumination and contrast quality. Then,...
Recognizing faces in presence of illuminations, pose, facial expression variations in controlled as well as uncontrolled environments remains one of the most challenging aspect. In this paper, we propose a novel recognition methodology which deals with challenges of face recognition to obtain robust and efficient recognition. The framework is based on extracting discriminant statistical features from...
Local ternary pattern (LTP) is a noise-robust version of local binary pattern (LBP). They are both encoding for the differences between the intensity of the center pixel and its neighborhoods. In this paper, based on Webers law we propose two new local descriptors, named Weber binary pattern (WBP) and Weber ternary pattern (WTP), which utilize binary and ternary encoding separately for the evaluation...
Local ordinal signal relations, such as local binary or ternary patterns (LBP/LTPs) are invariant to frequent in practice spatially variant contrast/offset deviations that preserve image appearance. Our prior work extended this conventional LBP/LTP-based classifiers towards learning, rather than pre-scribing characteristic shapes, sizes, and numbers of such patterns. The learned LTPs showed more accurate...
Semi-supervised learning approach is a fusion approach of supervised and unsupervised learning. Semi-supervised approach performs data learning from a limited number of available labelled training images along with a large pool of unlabelled data. Semi-supervised discriminant analysis (SDA) is one of the popular semi-supervised techniques. However, there is room for improvement. SDA resides in the...
This paper presents a method for face recognition using local directional number pattern (LDN). LDN adopts encoding the directional pattern of the face and produces a more selective code than methods listed in the literature. The directional patterns are computed using the compass mask and the information is encoded using the prominent direction indices (directional numbers) and sign — which distinguishes...
In this work, we present a new approach for gender classification problem by combining two different types of local features extracted from face images. Given one input image, a Elliptical Local Binary Patterns (ELBP) operator and a Local Phase Quantization (LPQ) operator are applied to generate two pattern images. Then, each pattern image is divided into disjoint rectangular sub-regions to compute...
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