Local descriptors in application to the aging problem in face recognition

Bereta, Michał; Karczmarek, Paweł; Pedrycz, Witold; Reformat, Marek

doi:10.1016/j.patcog.2013.03.010

Cited by 67 publications

(36 citation statements)

References 38 publications

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“…Image-based age estimation approaches view the face image as a texture pattern. Many texture features have been used like Local Binary Patterns (LBP) [9], Histograms of Oriented Gradients (HOG) [10], BIF, Binarized Statistical Image Features (BSIF) [11] and Local Phase Quantization (LPQ) in demographic estimation works. BIF and its variants are widely used in age estimation works such us [12,13,14].…”

Section: Related Workmentioning

confidence: 99%

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Comparative Study of Human Age Estimation Based on Hand-Crafted and Deep Face Features

Belver

Arganda‐Carreras

Dornaika

2017

Video Analytics. Face and Facial Expression Recognition and Audience Measurement

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In the past few years, human facial age estimation has drawn a lot of attention in the computer vision and pattern recognition communities because of its important applications in age-based image retrieval, security control and surveillance, biometrics, human-computer interaction (HCI) and social robotics. In connection with these investigations, estimating the age of a person from the numerical analysis of his/her face image is a relatively new topic. Also, in problems such as Image Classification the Deep Neural Networks have given the best results in some areas including age estimation.In this work we use three hand-crafted features as well as five deep features that can be obtained from pre-trained deep convolutional neural networks. We do a comparative study of the obtained age estimation results with these features.

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Section: Related Workmentioning

confidence: 99%

“…The original LBP operator labels the pixels of an image with decimal numbers, which are called LBPs or LBP codes that encode the local structure around each pixel [17,9]. The basic operator proceeds as follows.…”

Section: Local Binary Patterns (Lbp)mentioning

confidence: 99%

Comparative Study of Human Age Estimation Based on Hand-Crafted and Deep Face Features

Belver

Arganda‐Carreras

Dornaika

2017

Video Analytics. Face and Facial Expression Recognition and Audience Measurement

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“…Bereta et al [15] Sungatullina et al [16] presented a multiview discriminative learning (MDL) method that learns about a latent low dimensional subspace. It does so by projecting three local features (SIFT, LBP, and GOP) into a common feature space, such that the correlations of different feature representations of each sample are maximized, the within-class variation of each feature is minimized, and the betweenclass variation of each feature is maximized.…”

Section: Related Workmentioning

confidence: 99%

Age Invariant Face Recognition Based on DCT Feature Extraction and Kernel Fisher Analysis

2016

J Inf Process Syst

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The aim of this paper is to examine the effectiveness of combining three popular tools used in pattern recognition, which are the Active Appearance Model (AAM), the two-dimensional discrete cosine transform (2D-DCT), and Kernel Fisher Analysis (KFA), for face recognition across age variations. For this purpose, we first used AAM to generate an AAM-based face representation; then, we applied 2D-DCT to get the descriptor of the image; and finally, we used a multiclass KFA for dimension reduction. Classification was made through a K-nearest neighbor classifier, based on Euclidean distance. Our experimental results on face images, which were obtained from the publicly available FG-NET face database, showed that the proposed descriptor worked satisfactorily for both face identification and verification across age progression.

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“…Several LBP variants have been developed recently to improve performance in different applications [11], [12], [13]. These variants focus on different aspects of the original LBP operator.…”

Section: B Region Representationmentioning

confidence: 99%

Object Classification using Hybrid Holistic Descriptors: Application to Building Detection in Aerial Orthophotos

et al. 2015

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Abstract-We present a framework for automatic and accurate multiple detection of objects of interest from images using hybrid image descriptors. The proposed framework combines a powerful segmentation algorithm with a hybrid descriptor. The hybrid descriptor is composed by color histograms and several Local Binary Patterns based descriptors. The proposed framework involves two main steps. The first one consists in segmenting the image into homogeneous regions. In the second step, in order to separate the objects of interest and the image background, the hybrid descriptor of each region is classified using machine learning tools and a gallery of training descriptors. To show its performance, the method is applied to extract building roofs from orthophotos. We provide evaluation performances over 100 buildings. The proposed approach presents several advantages in terms of applicability, suitability and simplicity. We also show that the use of hybrid descriptors lead to an enhanced performance.

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Local descriptors in application to the aging problem in face recognition

Cited by 67 publications

References 38 publications

Comparative Study of Human Age Estimation Based on Hand-Crafted and Deep Face Features

Comparative Study of Human Age Estimation Based on Hand-Crafted and Deep Face Features

Age Invariant Face Recognition Based on DCT Feature Extraction and Kernel Fisher Analysis

Object Classification using Hybrid Holistic Descriptors: Application to Building Detection in Aerial Orthophotos

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