125 research outputs found
An Extensive Review on Spectral Imaging in Biometric Systems: Challenges and Advancements
Spectral imaging has recently gained traction for face recognition in
biometric systems. We investigate the merits of spectral imaging for face
recognition and the current challenges that hamper the widespread deployment of
spectral sensors for face recognition. The reliability of conventional face
recognition systems operating in the visible range is compromised by
illumination changes, pose variations and spoof attacks. Recent works have
reaped the benefits of spectral imaging to counter these limitations in
surveillance activities (defence, airport security checks, etc.). However, the
implementation of this technology for biometrics, is still in its infancy due
to multiple reasons. We present an overview of the existing work in the domain
of spectral imaging for face recognition, different types of modalities and
their assessment, availability of public databases for sake of reproducible
research as well as evaluation of algorithms, and recent advancements in the
field, such as, the use of deep learning-based methods for recognizing faces
from spectral images
Facial Attribute Capsules for Noise Face Super Resolution
Existing face super-resolution (SR) methods mainly assume the input image to
be noise-free. Their performance degrades drastically when applied to
real-world scenarios where the input image is always contaminated by noise. In
this paper, we propose a Facial Attribute Capsules Network (FACN) to deal with
the problem of high-scale super-resolution of noisy face image. Capsule is a
group of neurons whose activity vector models different properties of the same
entity. Inspired by the concept of capsule, we propose an integrated
representation model of facial information, which named Facial Attribute
Capsule (FAC). In the SR processing, we first generated a group of FACs from
the input LR face, and then reconstructed the HR face from this group of FACs.
Aiming to effectively improve the robustness of FAC to noise, we generate FAC
in semantic, probabilistic and facial attributes manners by means of integrated
learning strategy. Each FAC can be divided into two sub-capsules: Semantic
Capsule (SC) and Probabilistic Capsule (PC). Them describe an explicit facial
attribute in detail from two aspects of semantic representation and probability
distribution. The group of FACs model an image as a combination of facial
attribute information in the semantic space and probabilistic space by an
attribute-disentangling way. The diverse FACs could better combine the face
prior information to generate the face images with fine-grained semantic
attributes. Extensive benchmark experiments show that our method achieves
superior hallucination results and outperforms state-of-the-art for very low
resolution (LR) noise face image super resolution.Comment: To appear in AAAI 202
Eigen-patch iris super-resolution for iris recognition improvement
Low image resolution will be a predominant factor in iris recognition systems as they evolve towards more relaxed acquisition conditions. Here, we propose a super-resolution technique to enhance iris images based on Principal Component Analysis (PCA) Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information and reducing artifacts. We validate the system used a database of 1,872 near-infrared iris images. Results show the superiority of the presented approach over bilinear or bicubic interpolation, with the eigen-patch method being more resilient to image resolution reduction. We also perform recognition experiments with an iris matcher based 1D Log-Gabor, demonstrating that verification rates degrades more rapidly with bilinear or bicubic interpolation.peer-reviewe
A Practical Case Study: Face Recognition on Low Quality Images Using Gabor Wavelet and Support Vector Machines
Face recognition is a problem that arises on many real world applications, such as those related with Ambient Intelligence (AmI). The specific nature and goals of AmI applications, however, requires minimizing the invasiveness of data collection methods, often resulting in a drastic reduction of data quality and a plague of unforeseen effects which can put standard face recognition systems out of action. In order to deal with this, a face recognition system for AmI applications must not only be carefully designed but also subject to an exhaustive configuration plan to ensure it offers the required accuracy, robustness and real-time performance. This document covers the design and tuning of a holistic face recognition system targeting an Ambient Intelligence scenario. It has to work under partially uncontrolled capturing conditions: frontal images with pose variation up to 40 degrees, changing illumination, variable image size and degraded quality. The proposed system is based on Support Vector Machine (SVM) classifiers and applies Gabor Filters intensively. A complete sensitivity analysis shows how the recognition accuracy can be boosted through careful configuration and proper parameter setting, although the most adequate setting depends on the requirements for the final system.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB,CAMMADRINET S-0505 /TIC/0255 and DPS2008-07029-C02-02.Publicad
Face recognition using assemble of low frequency of DCT features
Face recognition is a challenge due to facial expression, direction, light, and scale variations. The system requires a suitable algorithm to perform recognition task in order to reduce the system complexity. This paper focuses on a development of a new local feature extraction in frequency domain to reduce dimension of feature space. In the propose method, assemble of DCT coefficients are used to extract important features and reduces the features vector. PCA is performed to further reduce feature dimension by using linear projection of original image. The proposed of assemble low frequency coefficients and features reduction method is able to increase discriminant power in low dimensional feature space. The classification is performed by using the Euclidean distance score between the projection of test and train images. The algorithm is implemented on DSP processor which has the same performance as PC based. The experiment is conducted using ORL standard face databases the best performance achieved by this method is 100%. The execution time to recognize 40 peoples is 0.3313 second when tested using DSP processor. The proposed method has a high degree of recognition accuracy and fast computational time when implemented in embedded platform such as DSP processor
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