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Proceedings ArticleDOI

Person Authentication Using Head Images

TL;DR: The experiments suggest that head images can be effectively used to ascertain human identity and the availability of this database could pave further research in this field.
Abstract: In many surveillance applications, the cameras are placed at overhead heights for human identification. In such real-world scenarios, the person of interest might be walking away from the camera and the only information available is "image of the person's head". In this research, we investigate the usage of head images for person recognition and propose it as a soft-biometric modality. With its viability for human recognition, application of head images can also be extended with other face recognition algorithms for surveillance. We propose a head image database pertaining to 103 subjects with more than 600 images. In addition to the database, we propose a framework for head image-based person verification. As a pre-processing stage, the framework includes evaluation of two segmentation algorithms. We also perform benchmarking evaluations of various texture, key-point, and learning-based representation algorithms and establish the baseline results. The experiments suggest that head images can be effectively used to ascertain human identity and the availability of this database could pave further research in this field.
Citations
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01 Jan 2006

3,012 citations

18 Jan 2010
TL;DR: In this paper, the authors proposed three part (head, torso, legs) height and colour soft biometric models, and demonstrate their verification performance on a subset of the PETS 2006 database.
Abstract: Soft biometrics are characteristics that can be used to describe, but not uniquely identify an individual. These include traits such as height, weight, gender, hair, skin and clothing colour. Unlike traditional biometrics (i.e. face, voice) which require cooperation from the subject, soft biometrics can be acquired by surveillance cameras at range without any user cooperation. Whilst these traits cannot provide robust authentication, they can be used to provide coarse authentication or identification at long range, locate a subject who has been previously seen or who matches a description, as well as aid in object tracking. In this paper we propose three part (head, torso, legs) height and colour soft biometric models, and demonstrate their verification performance on a subset of the PETS 2006 database. We show that these models, whilst not as accurate as traditional biometrics, can still achieve acceptable rates of accuracy in situations where traditional biometrics cannot be applied.

56 citations

Journal ArticleDOI
TL;DR: This paper presents a two-stage head detection framework that utilizes fully convolutional network (FCN) to generate scale-aware proposals followed by CNN that classifies each proposal into two classes, i.e. head and background.
Abstract: Pedestrian head detection plays an important role in identifying and localizing individuals in real world visual data. Head detection is a nontrivial problem due to considerable variance in camera view-points, scales, human poses, and appearances in the scene. Thanks to the translation invariance property of convolutional neural networks (CNNs) which enables large capacity CNNs to handle the problem of appearance and pose variations in the scene. However, the problem of scale invariance is still an open issue. To address this problem, this paper presents a two-stage head detection framework that utilizes fully convolutional network (FCN) to generate scale-aware proposals followed by CNN that classifies each proposal into two classes, i.e. head and background. Experiments results show that using scale-aware proposals obtained by FCN, the object recall rate and mean average precision (mAP) are improved. Additionaly, we demonstrate that our framework achieved state-of-the-art results on four challenging benchmark datasets, i.e. HollywoodHeads, Casablanca, SHOCK, and WIDERFACE.

7 citations


Cites methods from "Person Authentication Using Head Im..."

  • ...head detection is an important element and used as a pre-processing step in many video surveillance applications, for example, tracking [3], [12], person authentication [25] and density estimation [36]....

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Proceedings ArticleDOI
01 Jan 2019
TL;DR: A dual-pathway framework which computes head and body discriminating features independently, and learns the correlation between such features, and achieves promising experimental results on small and challenging datasets.
Abstract: In the light of the human studies that report a strong correlation between head circumference and body size, we propose a new research problem: head-body matching. Given an image of a person's head, we want to match it with his body (headless) image. We propose a dual-pathway framework which computes head and body discriminating features independently, and learns the correlation between such features. We introduce a comprehensive evaluation of our proposed framework for this problem using different features including anthropometric features and deep-CNN features, different experimental setting such as head-body scale variations, and different body parts. We demonstrate the usefulness of our framework with two novel applications: head/body recognition, and T-shirt sizing from a head image. Our evaluations for head/body recognition application on the challenging large scale PIPA dataset (contains high variations of pose, viewpoint, and occlusion) show up to 53% of performance improvement using deep-CNN features, over the global model features in which head and body features are not separated or correlated. For T-shirt sizing application, we use anthropometric features for head-body matching. We achieve promising experimental results on small and challenging datasets.

3 citations


Cites background from "Person Authentication Using Head Im..."

  • ...Mostly one body part is utilized, such as faces [25, 37, 21, 4], heads [31, 24], or fullbodies [11, 26, 30]....

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References
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Journal ArticleDOI
TL;DR: A review of the Pascal Visual Object Classes challenge from 2008-2012 and an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges.
Abstract: The Pascal Visual Object Classes (VOC) challenge consists of two components: (i) a publicly available dataset of images together with ground truth annotation and standardised evaluation software; and (ii) an annual competition and workshop. There are five challenges: classification, detection, segmentation, action classification, and person layout. In this paper we provide a review of the challenge from 2008---2012. The paper is intended for two audiences: algorithm designers, researchers who want to see what the state of the art is, as measured by performance on the VOC datasets, along with the limitations and weak points of the current generation of algorithms; and, challenge designers, who want to see what we as organisers have learnt from the process and our recommendations for the organisation of future challenges. To analyse the performance of submitted algorithms on the VOC datasets we introduce a number of novel evaluation methods: a bootstrapping method for determining whether differences in the performance of two algorithms are significant or not; a normalised average precision so that performance can be compared across classes with different proportions of positive instances; a clustering method for visualising the performance across multiple algorithms so that the hard and easy images can be identified; and the use of a joint classifier over the submitted algorithms in order to measure their complementarity and combined performance. We also analyse the community's progress through time using the methods of Hoiem et al. (Proceedings of European Conference on Computer Vision, 2012) to identify the types of occurring errors. We conclude the paper with an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges.

6,061 citations

01 Jan 2006

3,012 citations


"Person Authentication Using Head Im..." refers methods in this paper

  • ...The experiments are performed using a model pretrained on the Pascal VOC dataset [5]....

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01 Jan 2006
TL;DR: This report presents the results of the 2006 PASCAL Visual Object Classes Challenge (VOC2006).
Abstract: This report presents the results of the 2006 PASCAL Visual Object Classes Challenge (VOC2006). Details of the challenge, data, and evaluation are presented. Participants in the challenge submitted descriptions of their methods, and these have been included verbatim. This document should be considered preliminary, and subject to change.

2,034 citations

Journal ArticleDOI
TL;DR: It is demonstrated that integrating the information extracted from multiresolution SAR models gives much better performance than single resolution methods in both texture classification and texture segmentation.

762 citations


"Person Authentication Using Head Im..." refers methods in this paper

  • ...These features included: (i) macro-texture (orientation and length), (ii) shape, (iii) color, and (iv) features computed using MRRISAR model [11]....

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Journal ArticleDOI
TL;DR: Experimental results show that the use of soft biometric traits is able to improve the face-recognition performance of a state-of-the-art commercial matcher.
Abstract: Soft biometric traits embedded in a face (e.g., gender and facial marks) are ancillary information and are not fully distinctive by themselves in face-recognition tasks. However, this information can be explicitly combined with face matching score to improve the overall face-recognition accuracy. Moreover, in certain application domains, e.g., visual surveillance, where a face image is occluded or is captured in off-frontal pose, soft biometric traits can provide even more valuable information for face matching or retrieval. Facial marks can also be useful to differentiate identical twins whose global facial appearances are very similar. The similarities found from soft biometrics can also be useful as a source of evidence in courts of law because they are more descriptive than the numerical matching scores generated by a traditional face matcher. We propose to utilize demographic information (e.g., gender and ethnicity) and facial marks (e.g., scars, moles, and freckles) for improving face image matching and retrieval performance. An automatic facial mark detection method has been developed that uses (1) the active appearance model for locating primary facial features (e.g., eyes, nose, and mouth), (2) the Laplacian-of-Gaussian blob detection, and (3) morphological operators. Experimental results based on the FERET database (426 images of 213 subjects) and two mugshot databases from the forensic domain (1225 images of 671 subjects and 10 000 images of 10 000 subjects, respectively) show that the use of soft biometric traits is able to improve the face-recognition performance of a state-of-the-art commercial matcher.

239 citations


"Person Authentication Using Head Im..." refers background in this paper

  • ...In such cases, soft biometrics [4], [15], [17] act as an alternative to aid the performance of recognition systems....

    [...]