An Active Age Estimation of Facial image using Anthropometric Model and Fast ICA

Kumar, Santhosh; Ranjitha, S.; Suresh, H. N.

doi:10.25103/jestr.101.14

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…A set of landmark points is identified on the eyes, lips, nose, eyebrows, ears, chin, and forehead. Then, a number of measurements, like axial distance, the angle between components, shortest distance, tangential distance, angle of inclination, and ratios of distances can be calculated among face components [9]. In [10,11] the authors relied on anthropometrical measurements in the process of age group classification using Artificial Neural Networks (ANNs), while in Thukral et al [12], a hierarchical approach was applied in the process of human age estimation using 2D landmarks as shape features to train Support Vector Machine (SVM) and Support Vector Regression (SVR).…”

Section: Traditional Computer Vision-based Age Estimationmentioning

confidence: 99%

Semantic Human Face Analysis for Multi-level Age Estimation

Howyan¹,

Jaha²

2022

Intelligent Automation &Amp; Soft Computing

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Human face is one of the most widely used biometrics based on computer-vision to derive various useful information such as gender, ethnicity, age, and even identity. Facial age estimation has received great attention during the last decades because of its influence in many applications, like face recognition and verification, which may be affected by aging changes and signs which appear on human face along with age progression. Thus, it becomes a prominent challenge for many researchers. One of the most influential factors on age estimation is the type of features used in the model training process. Computer-vision is characterized by its superior ability to extract traditional facial features such as shape, size, texture, and deep features. However, it is still difficult for computers to extract and deal with semantic features inferred by human-vision. Therefore, we need somehow to bridge the semantic gap between machines and humans to enable utilization of the human brain capabilities of perceiving and processing visual information in semantic space. Our research aims to exploit human-vision in semantic facial feature extraction and fusion with traditional computer-vision features to obtain integrated and more informative features as an initial study paving the way to further augment the outperforming state-of-the-art age estimation models. A hierarchical automatic age estimation is achieved upon two consecutive stages: classification to predict (high-level) age group, followed by regression to estimate (low-level) exact age. The results showed noticeable performance improvements, when fusing semantic-based features with traditional vision-based features, surpassing the performance of traditional features alone.

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Section: Traditional Computer Vision-based Age Estimationmentioning

confidence: 99%

Semantic Human Face Analysis for Multi-level Age Estimation

Howyan¹,

Jaha²

2022

Intelligent Automation &Amp; Soft Computing

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“…The idea of using texture information of facial images is not new. However, improved multilevel LBP (mLBP) and Local Phase Quantization (LPQ) utilized to analyse age estimation on FG-NET, MORPH and PAL databases [4]. In our method, the original facial images normalized so that the orientation and the size of faces adjusted and only the facial region extracted.…”

Section: Introductionmentioning

confidence: 99%

Fusion Based Automatic Human Age Estimation Using Local Descriptors

.¹

2019

IJIES

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This paper introduces a new age estimation method based on the fusion of local features extracted using histogram-based local texture descriptors. In this study, the age estimation performances of well-known powerful texture descriptor with improved modification Local Binary Patterns (mLBP), and Local Phase Quantization (LPQ), which have not been analysed in depth for age estimation, investigated. In addition, multi-scale and spatial texture analysis performed for all descriptors. In the spatial texture analysis, a new approach using Overlapped Blocks obtained by discrete blocks proposed to reduce the overlapped age groups between the neighbour age group. Then feature fusion performed to investigate the age estimation accuracies of different combinations of local texture descriptors. Multiple Linear Regression (MLR) used to estimate the specific age at the end of this proposed system. The results show that the age estimation accuracy of the proposed method is better when compared to previous methods on FG-NET, MORPH databases.

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“…Facial aging is a problem in face recognition, because simulating the appearance of a person across years may help recognizing his or her face [10][11]. Most of the existing facial age estimation methods usually employ handcrafted feature descriptors like Local Binary Pattern (LBP), color moments, etc., for face representation, which require strong prior knowledge [12][13].…”

Section: Introductionmentioning

confidence: 99%

Overlapped Semantic Age Group Estimation Using Hybrid PCA and Log Gabor Filter

Gangadharaiah¹,

Suresh²

2018

IJIES

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Automatic extraction of soft biometric characteristics of face image is an emerging research field in recent years. Among these soft biometrics, age estimation is very useful for several applications, like video surveillance, business intelligence, and search optimization in large databases. Generally, facial aging effects perceived in two main forms like, growth related transformations and textural variation. So, in order to generate an effective age classifier, both dimension and texture information should be used together. In this work, an age estimation system that is combination of dimension feature (Hybrid Principal Component Analysis (HPCA)) and textural feature (Log Gabor filter) for feature extraction, is used. The extracted feature values are classified by employing a multi-objective classifier named as Multi-Support Vector Machine (M-SVM). The result of the experiment shows that the proposed approach delivers a better age estimation rate on overlapping age group classes.

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An Active Age Estimation of Facial image using Anthropometric Model and Fast ICA

Cited by 5 publications

References 13 publications

Semantic Human Face Analysis for Multi-level Age Estimation

Semantic Human Face Analysis for Multi-level Age Estimation

Fusion Based Automatic Human Age Estimation Using Local Descriptors

Overlapped Semantic Age Group Estimation Using Hybrid PCA and Log Gabor Filter

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