Vishnu Naresh Boddeti

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Alignment of 3D objects from 2D images is one of the most important and well studied problems in computer vision. A typical object alignment system consists of a landmark appearance model which is used to obtain an initial shape and a shape model which refines this initial shape by correcting the initialization errors. Since errors in landmark(More)
Support vector machine (SVM) classifiers are popular in many computer vision tasks. In most of them, the SVM classifier assumes that the object to be classified is centered in the query image, which might not always be valid, e.g., when locating and classifying a particular class of vehicles in a large scene. In this paper, we introduce a new classifier(More)
Biometrics systems typically work best in settings where probe samples are captured in the same manner as the training set. When biometrics are acquired under different conditions or with different sensors, naïve approaches to recognition perform poorly. Coupled mappings have been introduced for performing face recognition across different(More)
We consider the problem of designing a scene-specific pedestrian detector in a scenario where we have zero instances of real pedestrian data (i.e., no labeled real data or unsupervised real data). This scenario may arise when a new surveillance system is installed in a novel location and a scene-specific pedestrian detector must be trained prior to any(More)
Iris recognition can offer high-accuracy person recognition, particularly when the acquired iris image is well focused. However, in some practical scenarios, user cooperation may not be sufficient to acquire iris images in focus; therefore, iris recognition using camera systems with a large depth of field is very desirable. One approach to achieve extended(More)
Many scenarios require that face recognition be performed at conditions that are not optimal. Traditional face recognition algorithms are not best suited for matching images captured at a low-resolution to a set of high-resolution gallery images. To perform matching between images of different resolutions, this work proposes a method of learning two sets of(More)
The periocular region as a biometric trait has recently gained considerable traction, especially under challenging scenarios where reliable iris information is not available for human authentication. In this paper, we consider the problem of one-to-one (1 : 1) matching of highly nonideal periocular images captured in-the-wild under unconstrained imaging(More)
Iris recognition is believed to offer excellent recognition rates for iris images acquired under controlled conditions. However, recognition rates degrade considerably when images exhibit impairments such as off-axis gaze, partial occlusions, specular reflections and out-of-focus and motioninduced blur. In this paper, we use the recently-available face and(More)
We consider the problem of matching highly non-ideal ocular images where the iris information cannot be reliably used. Such images are characterized by non-uniform illumination, motion and de-focus blur, off-axis gaze, and non-linear deformations. To handle these variations, a single feature extraction and matching scheme is not sufficient. Therefore, we(More)
This chapter discusses the performance of 5 different iris segmentation algorithms on challenging periocular images. The goal is to convey some of the difficulties in localizing the iris structure in images of the eye characterized by variations in illumination, eye-lid and eye-lash occlusion, de-focus blur, motion blur and low resolution. The 5 algorithms(More)