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Large-scale identity projects such as the Unique Identification Authority of India (UIDAI) comprise of multiple individual organizations, which may use different sensors for enrolling the individuals while the data obtained at the time of verification can be collected from a different sensor. In such multi-camera scenario, it is imperative to perform image-based iris sensor identification. In this...
Ocular recognition algorithms, including iris matching, have been used in several applications including large scale national ID projects such as India's Aadaar. Deployment of large-scale biometric systems is expected to rely on using multiple devices including mobile devices to ensure widespread adoption of biometric recognition systems. Ocular images captured using mobile devices may have challenges...
Face identification from low quality and low resolution Near-Infrared (NIR) face images is a challenging problem. Since surveillance cameras typically acquire images at a large standoff distance, the effective resolution of the face is not large enough to identify the individuals. Moreover for a 24-hour surveillance footage, images in low light and at nighttime are acquired in NIR mode which makes...
Matching near-infrared to visible images is one of the heterogeneous face recognition challenges in which spectral variations cause changes in the appearance of face images. In this paper, we propose to utilize a keypoint selection approach in the recognition pipeline. The proposed keypoint selection approach is a fast approximation of feature selection approach, yielding two orders of magnitude improvement...
Latent fingerprints are lifted from multiple types of surfaces, which vary in material type, texture, color, and shape. These differences in the surfaces introduce significant intra-class variations in the lifted prints such as availability of partial print, background noise, and poor ridge structure quality. Due to these observed variations, the overall quality and the matching performance of latent...
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