Local descriptors and similarity measures for frontal face recognition: A comparative analysis

Bereta, Michał; Pedrycz, Witold; Reformat, Marek

doi:10.1016/j.jvcir.2013.08.004

Cited by 46 publications

(32 citation statements)

References 58 publications

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“…Each of these radial polynomials used corresponds to a different characteristic of the image [57]. In problems such as face recognition, rather than the holistic characterization of images, local statistics have more importance [14]. Therefore, the local Zernike moments (LZM) [39] transformation was proposed to extract local variations by calculating these moments around each pixel on the face images.…”

Section: Local Zernike Momentsmentioning

confidence: 99%

An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition

2018

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Abstract:In this study, we propose a face recognition scheme using local Zernike moments (LZM), which can be used for both identification and verification. In this scheme, local patches around the landmarks are extracted from the complex components obtained by LZM transformation. Then, phase magnitude histograms are constructed within these patches to create descriptors for face images. An image pyramid is utilized to extract features at multiple scales, and the descriptors are constructed for each image in this pyramid. We used three different public datasets to examine the performance of the proposed method:Face Recognition Technology (FERET), Labeled Faces in the Wild (LFW), and Surveillance Cameras Face (SCface). The results revealed that the proposed method is robust against variations such as illumination, facial expression, and pose. Aside from this, it can be used for low-resolution face images acquired in uncontrolled environments or in the infrared spectrum. Experimental results show that our method outperforms state-of-the-art methods on FERET and SCface datasets.

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Section: Local Zernike Momentsmentioning

confidence: 99%

An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition

2018

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“…In this case, the classification system performs hypothesis testing (or one-class classification) using a single reference face image per target individual. Template matching algorithms employ each facial model defined as a set of one or more templates stored in a gallery [6], [9]. It is also possible to consider a one-class classifier like Gaussian mixture modeling [29] or one-class SVMs [60] to learn from an abundance of non-target class samples that are somehow similar to the single target class sample.…”

Section: Face Classification Systemsmentioning

confidence: 99%

“…In particular, uniform non-overlapping patches are isolated in the reference ROI to improve robustness to occlusion [34]. Local Binary Pattern (LBP), Local Phase Quantization (LPQ), Histogram of Oriented Gradients (HOG), and Haar features are considered to extract information from patches to provide robustness to local changes in illumination, blur, etc [2], [3], [9], [16]. During operations, ROIs of faces captured in videos are classified by each individual-specific ensemble of the system, and the ensemble scores are combined.…”

Section: Introductionmentioning

confidence: 99%

Robust watch-list screening using dynamic ensembles of SVMs based on multiple face representations

Bashbaghi

Granger

Sabourin

et al. 2017

Machine Vision and Applications

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Abstract. Still-to-video face recognition (FR) is an important function in video surveillance, where faces captured over a network of video cameras are matched against reference stills of target individuals. Screening faces against a watch-list is a challenging video surveillance application because the appearance of faces vary due to changing capture conditions and operational domains. The facial models used for matching may not be representative of faces captured with video cameras because they are typically designed a priori with only one reference still. In this paper, a multi-classifier framework is proposed for robust still-to-video FR based on multiple and diverse face representations of a single reference face still. During enrollment of a target individual, the single reference face still is modeled using an ensemble of SVM classifiers based on different patches and face descriptors. Multiple feature extraction techniques are applied to patches isolated in the reference still to generate a diverse SVM pool that provides robustness to common nuisance factors (e.g., variations in illumination and pose). The estimation of discriminant feature subsets, classifier parameters, decision thresholds, and ensemble fusion functions is achieved using the high-quality reference still and a large number of faces captured in lower quality video of non-target individuals in the scene. During operations, the most competent subset of SVMs are dynamically selected according to capture conditions. Finally, a head-face tracker gradually regroups faces captured from different people appearing in a scene, while each individual-specific ensemble performs face matching. The accumulation of matching scores per face track leads to a robust spatio-temporal FR when accumulated ensemble scores surpass a detection threshold. Experimental results obtained with the Chokepoint and COX-S2V datasets show a significant improvement in performance w.r.t. reference systems, especially when individual-specific ensembles (1) are designed using exemplar-SVMs rather than one-class SVMs, and (2) exploit score-level fusion of local SVMs (trained using features extracted from each patch), rather than using either decision-level or feature-level fusion with a global SVM (trained by concatenating features extracted from patches).

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“…Accordingly, many (non metric) parametric dissimilarity measures could be designed depending on the specific task. Recently, there is a steady increasing interest in using several, possibly heterogeneous, dissimilarity measures at the same time [28,40,41,8,24]. Regardless of the number of dissimilarity measures, the setting of their characterizing parameters is what really allows to discover the relevant information hidden in the data.…”

Section: Introductionmentioning

confidence: 99%

“…For a given dissimilarity measure, it is possible to distinguish two main approaches [35]: those trying to determine a partition of data, and those that focus on searching for isolated clusters surrounded by uncategorized data. Local description of data is of particular interest, since it allows to characterize the input data by means of a heterogeneous collection of descriptions [8].…”

Section: Introductionmentioning

confidence: 99%

An agent-based algorithm exploiting multiple local dissimilarities for clusters mining and knowledge discovery

et al. 2015

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We propose a multi-agent algorithm able to automatically discover relevant regularities in a given dataset, determining at the same time the set of configurations of the adopted parametric dissimilarity measure yielding compact and separated clusters. Each agent operates independently by performing a Markovian random walk on a suitable weighted graph representation of the input dataset. Such a weighted graph representation is induced by the specific parameter configuration of the dissimilarity measure adopted by the agent, which searches and takes decisions autonomously for one cluster at a time.Results show that the algorithm is able to discover parameter configurations * Corresponding Author

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Local descriptors and similarity measures for frontal face recognition: A comparative analysis

Cited by 46 publications

References 58 publications

An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition

An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition

Robust watch-list screening using dynamic ensembles of SVMs based on multiple face representations

An agent-based algorithm exploiting multiple local dissimilarities for clusters mining and knowledge discovery

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