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Discriminative common feature subspace learning for age-invariant face recognition

IET BIOMETRICS 2020年第4期9卷 136-142页

作者： Yu, Yu-Feng Wang, Qiangchang Jiang, Min Guangzhou Univ Dept Stat Guangzhou 510006 Peoples R China West Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown WV 26506 USA

Considering human ageing has a big impact on cross-age face recognition, and the effect of ageing on face recognition in non-ideal images has not been well addressed yet. In this study, the authors propose a discriminative common feature subspace learning method to deal with the problem. Specifically, they consider the samples of the same individual with big age gaps have different distributions in the original space, and employ the maximum mean discrepancy as the distance measure to compute the distances between the sample means of the different distributions. Then the distance measure is integrated into Fisher criterion to learn a discriminative common feature subspace. The aim is to map the images with different ages to the common subspace, and to construct new feature representation which is robust to age variations and discriminative to different subjects. To evaluate the performance of the proposed method on cross-age face recognition, the authors construct extensive experiments on CACD and fg-net databases. Experimental results show that the proposed method outperforms other subspace based methods and state-of-art cross-age face recognition methods.

关键词： face recognition learning (artificial intelligence) image representation distance measure age variations age-invariant face recognition human ageing discriminative common feature subspace learning cross-age face recognition methods CACD database fg-net database Fisher criterion feature representation

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Facial aging simulation via tensor completion and metric learning

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IET COMPUTER VISION 2017年第1期11卷 78-86页

作者： Wang, Heng Huang, Di Wang, Yunhong Yang, Hongyu Beihang Univ Sch Comp Sci & Engn Beijing Key Lab Digital Media Lab Intelligent Recognit & Image Proc Beijing 100191 Peoples R China

Facial aging simulation is one of the most challenging issues in automatic machine based face analysis, where the most essential requirements are (i) human identity should remain stable in texture synthesis and (ii) the texture synthesised is expected to accord with human cognitive perception in aging. In this study, the authors propose a tensor completion based method to transform the simulation task to a standard matrix completion one. To protect human dependent characteristics during texture synthesis, the proposed method processes the two major components, i. e. identity and age, in different channels. Furthermore, they incorporate prior information in such a process, assuming that the textures of different subjects in the same age group are similar and similar looking people tend to age in similar ways, and the metric learning technique is adopted to measure the similarity between identities so that the faces that have the highest similarities with the one in the test image are assigned bigger weights in texture generation. In addition, shape deformation is also considered to make the synthesised images more natural. Experimental results achieved on the fg-net database demonstrate the effectiveness of the proposed method.

关键词： face recognition image texture learning (artificial intelligence) tensors matrix algebra digital simulation facial aging simulation tensor completion based method metric learning automatic machine based face analysis human identity texture synthesis human cognitive perception standard matrix completion similarity measurement shape deformation texture generation fg-net database

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Age-API: are landmarks-based features still distinctive for invariant facial age recognition?

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第26期83卷 67599-67625页

作者： Abbes, Amal Ouarda, Wael Ben Ayed, Yassine Univ Sfax Multimedia Informat Syst & Adv Comp Lab MIRACL Sfax Tunisia Sfax Univ Digital Res Ctr Sfax CRNS Lab Signals Syst aRtificial Intelligence neTworkS Sfax 3021 Tunisia

Age recognition from faces has attracted potential interest due to its importance in several areas as well as minor-security and online shopping. However, it still has various challenges, especially in an unconstrained environment such as anti-aging treatments, illumination conditions, and filter effects. To deal with these problems, we choose to extract geometric features from face images. Thus, our proposed method consists firstly, of verifying if the acquired image contains a face. Then, the landmarks coordinates are calculated and the image is divided into 4 blocks. In each block, cross-distances are calculated and only max distances are considered to compute ratios. Finally, distances and ratios-max distances are fused to represent the age. In relation with the insufficient data issues and real time responses requirement in the Application Programming Interface (API), we highlight the relative role of using the handcrafted-based features compared to deep learning method for the proposed purpose. On the other hand, imbalanced data and intra-class variability present another famous challenge aiming to decrease the performance results. To overcome these issues, we proposed a multi-level classification strategy. Our developed approach is evaluated using fg-net and Adience datasets, and the obtained results are effective compared to other recent existing works.

关键词： Soft-biometric Age recognition Adience dataset fg-net database SVM

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Age Estimation from Face Images: Challenging Problem for Audience Measurement Systems 16

Age Estimation from Face Images: Challenging Problem for Aud...

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16th FRUCT Conference of Open-Innovations-Association

作者： Khryashchev, Vladimir Ganin, Alexander Stepanova, Olga Lebedev, Anton Yaroslavl State Univ Yaroslavl Russia

ISBN: (纸本)9785757704890

The real-time audience measurement system consists of five consecutive stages: face detection, face tracking, gender recognition, age classification and in-cloud data statistics analysis. The challenging part of such system is age estimation algorithm on the basis of machine learning methods. The face aging process is determined by different factors: genetic, lifestyle, expression and environment. That is why same age people can have quite different rates of facial aging. We propose a novel algorithm consisting of two stages: adaptive feature extraction based on local binary patterns and support vector machine classification. Experimental results on the fg-net, MORPH and our own database are presented. Human perception ability in age estimation is studied using crowdsourcing which allows a comparison of the ability of machines and humans.

关键词： face recognition feature extraction image classification learning (artificial intelligence) object detection object tracking statistical analysis support vector machines fg-net database MORPH database adaptive feature extraction age classification stage age estimation audience measurement system crowdsourcing environment factor expression factor face detection stage face images face tracking stage facial aging gender recognition stage genetic factor in-cloud data statistics analysis stage lifestyle factor local binary pattern machine learning method support vector machine classification Algorithm design and analysis Classification algorithms databases Estimation Face Testing Training Accelerated aging Support vector machine classification Feature extraction Algorithm design and analysis Face Face Sorting algorithm Facial Recognition Genetic factors Object detection Image classification Support Vector network database Crowdsource binary pattern

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