Face recognition performance with superresolution

Hu, Shuowen; Maschal, Robert; Young, S. Susan; Hong, Tsai Hong; Phillips, P. Jonathon

doi:10.1364/ao.51.004250

Cited by 7 publications

(8 citation statements)

References 7 publications

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“…Although some information in the HiRes images will be lost in the down-sampling process, it has been reported by some researchers that this approach has similar recognition performance as SupRes methods for LoRes face recognition. For instance, Hu et al [20] conducted experiments using a video database of moving faces and people. Their experimental results show that applying SupRes methods and then comparing with HiRes galleries have similar performance as LoRes to LoRes comparison at a far range (5-10 pixel eye-to-eye distance).…”

Section: Lores Versus Ds Hires Comparisonmentioning

confidence: 99%

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Low‐resolution face alignment and recognition using mixed‐resolution classifiers

2017

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A very common case for law enforcement is recognition of suspects from a long distance or in a crowd. This is an important application for low-resolution face recognition (in the authors' case, face region below 40 × 40 pixels in size). Normally, high-resolution images of the suspects are used as references, which will lead to a resolution mismatch of the target and reference images since the target images are usually taken at a long distance and are of low resolution. Most existing methods that are designed to match high-resolution images cannot handle low-resolution probes well. In this study, they propose a novel method especially designed to compare low-resolution images with high-resolution ones, which is based on the log-likelihood ratio (LLR). In addition, they demonstrate the difference in recognition performance between real low-resolution images and images down-sampled from high-resolution ones. Misalignment is one of the most important issues in low-resolution face recognition. Two approaches-matching-score-based registration and extended training of images with various alignmentsare introduced to handle the alignment problem. Their experiments on real low-resolution face databases show that their methods outperform the state-of-the-art.

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Section: Lores Versus Ds Hires Comparisonmentioning

confidence: 99%

“…For instance, Hu et al . [20] conducted experiments using a video database of moving faces and people. Their experimental results show that applying SupRes methods and then comparing with HiRes galleries have similar performance as LoRes to LoRes comparison at a far range (5–10 pixel eye‐to‐eye distance).…”

Section: Approaches To Lores–hires Comparisonmentioning

confidence: 99%

Low‐resolution face alignment and recognition using mixed‐resolution classifiers

2017

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“…The techniques described previously seek to generate higher resolution face images with improved visual quality, but do not take discriminativity into account, which is the objective for face recognition. However, improving visual quality through super-resolution has been shown to improve face recognition algorithm performance [10]. To address the issue of discriminativity, recognition-oriented super-resolution techniques have been developed [11,12].…”

Section: Introductionmentioning

confidence: 99%

Dimensionality analysis of facial signatures in visible and thermal spectra

Short

Gurram

2015

SPIE Proceedings

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Face images are an important source of information for biometric recognition and intelligence gathering. While face recognition research has made significant progress over the past few decades, recognition of faces at extended ranges is still highly problematic. Recognition of a low-resolution probe face image from a gallery database, typically containing high resolution facial imagery, leads to lowered performance than traditional face recognition techniques. Learning and super-resolution based approaches have been proposed to improve face recognition at extended ranges; however, the resolution threshold for face recognition has not been examined extensively. Establishing a threshold resolution corresponding to the theoretical and empirical limitations of low resolution face recognition will allow algorithm developers to avoid focusing on improving performance where no distinguishable information for identification exists in the acquired signal. This work examines the intrinsic dimensionality of facial signatures and seeks to estimate a lower bound for the size of a face image required for recognition. We estimate a lower bound for face signatures in the visible and thermal spectra by conducting eigenanalysis using principal component analysis (PCA) (i.e., eigenfaces approach). We seek to estimate the intrinsic dimensionality of facial signatures, in terms of reconstruction error, by maximizing the amount of variance retained in the reconstructed dataset while minimizing the number of reconstruction components. Extending on this approach, we also examine the identification error to estimate the dimensionality lower bound for lowresolution to high-resolution (LR-to-HR) face recognition performance. Two multimodal face datasets are used for this study to evaluate the effects of dataset size and diversity on the underlying intrinsic dimensionality: 1) 50-subject NVESD face dataset (containing visible, MWIR, LWIR face imagery) and 2) 119-subject WSRI face dataset (containing visible and MWIR face imagery).

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“…Python wrapper for dlib and OpenCV libraries3 Adaboost Haar Cascade classifier is known to have higher face detection rate but with large number of false positives[19,18] …”

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confidence: 99%

Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild

El-Sayed

Mahmood

Sobh

2017

2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

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Majority of the face recognition algorithms use query faces captured from uncontrolled, in the wild, environment. Often caused by the cameras' limited capabilities, it is common for these captured facial images to be blurred or low resolution. Super resolution algorithms are therefore crucial in improving the resolution of such images especially when the image size is small requiring enlargement. This paper aims to demonstrate the effect of one of the state-of-the-art algorithms in the field of image super resolution. To demonstrate the functionality of the algorithm, various before and after 3D face alignment cases are provided using the images from the Labeled Faces in the Wild (lfw). Resulting images are subject to testing on a closed set face recognition protocol using unsupervised algorithms with high dimension extracted features. The inclusion of super resolution algorithm resulted in significant improved recognition rate over recently reported results obtained from unsupervised algorithms.Index Terms-Super-Resolution, high dimensions features, unsupervised learning, face recognition, label faces in the wild (lfw)

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Face recognition performance with superresolution

Cited by 7 publications

References 7 publications

Low‐resolution face alignment and recognition using mixed‐resolution classifiers

Low‐resolution face alignment and recognition using mixed‐resolution classifiers

Dimensionality analysis of facial signatures in visible and thermal spectra

Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild

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