Multi‐stage age estimation using two level fusions of handcrafted and learned features on facial images

Taheri, Shahram; Toygar, Önsen

doi:10.1049/iet-bmt.2018.5141

Cited by 18 publications

(7 citation statements)

References 50 publications

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“…This model includes two approaches: the first approach is based on a feature-level fusion of several local handcrafted features of wrinkles, skin with some other Biologically Inspired Features (BIFs), and the second approach is score-level fusion of feature vectors learned from a CNN with multiple layers. In [24], a new CNN architecture is introduced as Directed Acyclic Graph Convolutional Neural Networks (DAG-CNN) for estimating human age, which automatically learns discriminative features obtained from different layers of a GoogLeNet CNN [25] and VGG16 CNN [26] models and combines them together. As such, in [24], they built two variant architectures.…”

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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“…In [88], Taheri et al proposed the combination of different type of feature extraction methods for accurate facial age estimation, which is performed by using two-level fusion of features and scores. In [89], Taheri et al proposed a new age estimation method which exploits multi-stage features from a generic feature extractor, a trained CNN, and precisely combined these features with a selection of age-related handcrafted features. This method adopts a decision-level fusion of estimated ages by two different approaches; the first one utilizes feature-level fusion of different handcrafted local feature descriptors for wrinkle, skin and facial component, while the second one utilizes score-level fusion of different feature layers of a CNN.…”

Section: Related Workmentioning

confidence: 99%

Age Estimation by Super-Resolution Reconstruction Based on Adversarial Networks

et al. 2020

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Age estimation using facial images is applicable in various fields, such as age-targeted marketing, analysis of demand and preference for goods, skin care, remote medical service, and age statistics, for describing a specific place. However, if a low-resolution camera is used to capture the images, or facial images are obtained from the subjects standing afar, the resolution of the images is degraded. In such a case, information regarding wrinkles and the texture of the face are lost, and features that are crucial for age estimation cannot be obtained. Existing studies on age estimation did not consider the degradation of resolution but used only high-resolution facial images. To overcome this limitation, this paper proposes a deep convolutional neural network (CNN)-based age estimation method that reconstructs low-resolution facial images as high-resolution images using a conditional generative adversarial network (GAN), and then uses the images as inputs. An experiment is conducted using two open databases (PAL and MORPH databases). The results demonstrate that the proposed method achieves higher accuracy in high-resolution reconstruction and age estimation than the state-of-the art methods.

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“…Local Binary Pattern (LBP): This is a simple yet effective pixel-based texture descriptor that was originally proposed by Ojala et al [51] LBP is one of the most commonly used hand-crafted feature extraction methods in gender recognition [31,34,69,71,[82][83][84]. The original descriptor assigns a binary digit for each pixel in a 3 × 3 neighborhood by comparing their pixel intensity values with the central pixel, which acts as a threshold.…”

Section: Hand-crafted Featuresmentioning

confidence: 99%

Face Gender Recognition in the Wild: An Extensive Performance Comparison of Deep-Learned, Hand-Crafted, and Fused Features with Deep and Traditional Models

et al. 2020

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Face gender recognition has many useful applications in human–robot interactions as it can improve the overall user experience. Support vector machines (SVM) and convolutional neural networks (CNNs) have been used successfully in this domain. Researchers have shown an increased interest in comparing and combining different feature extraction paradigms, including deep-learned features, hand-crafted features, and the fusion of both features. Related research in face gender recognition has been mostly restricted to limited comparisons of the deep-learned and fused features with the CNN model or only deep-learned features with the CNN and SVM models. In this work, we perform a comprehensive comparative study to analyze the classification performance of two widely used learning models (i.e., CNN and SVM), when they are combined with seven features that include hand-crafted, deep-learned, and fused features. The experiments were performed using two challenging unconstrained datasets, namely, Adience and Labeled Faces in the Wild. Further, we used T-tests to assess the statistical significance of the differences in performances with respect to the accuracy, f-score, and area under the curve. Our results proved that SVMs showed best performance with fused features, whereas CNN showed the best performance with deep-learned features. CNN outperformed SVM significantly at p < 0.05.

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Multi‐stage age estimation using two level fusions of handcrafted and learned features on facial images

Cited by 18 publications

References 50 publications

Semantic Human Face Analysis for Multi-level Age Estimation

Semantic Human Face Analysis for Multi-level Age Estimation

Age Estimation by Super-Resolution Reconstruction Based on Adversarial Networks

Face Gender Recognition in the Wild: An Extensive Performance Comparison of Deep-Learned, Hand-Crafted, and Fused Features with Deep and Traditional Models

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