Low-resolution face recognition: a review

Wang, Zhifei; Miao, Zhenjiang; Wu, Q. M. Jonathan; Wan, Yuanyuan; Tang, Zhen

doi:10.1007/s00371-013-0861-x

Cited by 112 publications

(58 citation statements)

References 67 publications

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“…A popular approach to boost lowresolution face recognition is to apply super-resolution to perform further analysis on the resulting high-resolution images [16], or incorporate super-resolution directly into face recognition [4]. This is especially successful if the low-resolution probe faces should be compared to highresolution gallery faces [17,11]. When matching lowresolution to further low-resolution faces, adjustments of the used features are a common solution [5].…”

Section: Resolutionmentioning

confidence: 98%

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Impact of resolution and image quality on video face analysis

Herrmann

Willersinn

et al. 2015

2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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Low-resolution face analysis suffers more significantly from quality degradations than high-resolution analysis. In this work, we will investigate how several face analysis steps are influenced by low image quality and how this relates to the low resolution. In the first step, a simulation of different effects on image quality, namely low resolution, compression artifacts, motion blur and noise is performed and the impact on face detection, registration and recognition is analyzed. Depending on the situation, it becomes obvious that the low resolution is sometimes a minor degrading effect, outmatched by a single one or a combination of the further effects. When addressing real-world face recognition from surveillance data, the combination of the challenging effects is the biggest problem because typical counter measures are individual to one single effect

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Section: Resolutionmentioning

confidence: 98%

“…According to [17] the biggest problems for low-resolution face recognition are misalignment, noise affection, lack of effective features and dimensional mismatch between probe and gallery. This list addresses the quality problem only roughly by combining all quality flaws under the term noise affection.…”

Section: Introductionmentioning

confidence: 99%

Impact of resolution and image quality on video face analysis

Herrmann

Willersinn

et al. 2015

2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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“…Head matching: Many sophisticated face recognition methods have been proposed in the literature, and they generally use the inner region of the face for recognition. However, these methods often cannot achieve reasonable accuracy when the targeted face is extremely small [57,58]. We therefore used the texture information from the head region, including the inner face region, the hair and the face contour parts in this paper, and call it the head feature of the target subject because our feature differs from the general face feature.…”

Section: Matching Algorithm For Score Calculationmentioning

confidence: 99%

MultiQ: single sensor-based multi-quality multi-modal large-scale biometric score database and its performance evaluation

Uddin

Muramatsu

Kimura

et al. 2017

IPSJ T Comput Vis Appl

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Single sensor-based multi-modal biometrics is a promising approach that offers simple system construction, low cost, and wide applicability to real situations such as CCTV footage-based criminal investigations. In multi-modal biometrics, fusion at the score-level is a popular and promising approach, and data qualities that affect the matching score of each modality are often incorporated as a quality-dependent score-level fusion framework. This paper presents a very large-scale single sensor-based multi-quality multi-modal biometric score database called MultiQ Score Database version 2 to advance the research into evaluation, comparison, and benchmarking of score-level fusion approaches using both quality-independent and quality-dependent protocols. We extracted gait, head, and height modalities from the OU-ISIR Gait Database and introduce spatial resolution (SR), temporal resolution (TR) and view as quality measures that significantly affect biometric system performance. We considered seven and 10 scaling factors for SR and TR, respectively, with four view variations. We then constructed a database comprising approximately 4 million genuine and 7.5 billion imposter score databases. To evaluate this database, we set two different protocols, and provided a set recognition accuracy for state-of-the-art approaches using protocols for both quality-independent and quality-dependent schemes. This database and the evaluation results will be beneficial for score-level fusion research. Additionally, we provide detailed analysis of the recognition accuracies associated with gait, head, and height modalities in different spatial/temporal resolutions and views. These analyses may be useful in criminal investigation research.

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“…An excellent review of these techniques is given in [4]. Super-resolution based techniques either reconstruct a higher resolution face image from a sequence of low-resolution frames, or synthesize a higher resolution face image from a single low resolution image using face priors.…”

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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Low-resolution face recognition: a review

Cited by 112 publications

References 67 publications

Impact of resolution and image quality on video face analysis

Impact of resolution and image quality on video face analysis

MultiQ: single sensor-based multi-quality multi-modal large-scale biometric score database and its performance evaluation

Dimensionality analysis of facial signatures in visible and thermal spectra

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