Discriminating projections for estimating face age in wild images

Tokola, Ryan; Bolme, David S.; Boehnen, Christopher; Barstow, Del; Ricanek, Karl

doi:10.1109/btas.2014.6996287

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…Later, Hajizadeh and Ebrahimnezhad [76] used Probabilistic Neural Network (PNN) as a classifier and achieved better results on IFDB by extracting HOG features. Using projections of pose specific, Tokola et al [109] tried to decrease the influence of the variations in the image. The image features were mapped into a latent space which is insensitive to pose.…”

Section: A Classification-based Methodsmentioning

confidence: 99%

Comprehensive Analysis of the Literature for Age Estimation From Facial Images

Al-Shannaq

Elrefaei

2019

IEEE Access

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Recently, vast attention has grown in the field of computer vision, especially in face recognition, detection, and facial landmarks localization. Many significant features can be directly derived from the human face, such as age, gender, and race. Estimating the age can be defined as the automatic process of classifying the facial image into the exact age or to a specific age range. Practically, age estimation from the face is still a challenging problem due to the effects from many internal factors, such as gender and race, and external factors, such as environments and lifestyle. Huge efforts have been addressed to reach an accepted and satisfied accuracy of age estimation task. In this paper, we try to analyze the main aspects that can increase the performance of the age estimation system, present the handcrafted-based models and deep learning-based models, and show how the evaluations are being conducted, discuss the proposed algorithms and models in the age estimation, and show the main limitations and challenges facing the age estimation process. Also, different aging databases that contain age annotations are discussed. Finally, few guidelines and the future prospect related to the age estimation are investigated.

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Section: A Classification-based Methodsmentioning

confidence: 99%

Comprehensive Analysis of the Literature for Age Estimation From Facial Images

Al-Shannaq

Elrefaei

2019

IEEE Access

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“…Just as image-based biometrics performance can be improved by combining PCA projections of images with other features [16] [12], CVs could contribute a robust description of 3D shape to an ensemble of more specialized features. We expect CVs to enhance the performance of models with problem-specific features.…”

Section: Future Workmentioning

confidence: 99%

3D face analysis for demographic biometrics

Tokola

Mikkilineni

Boehnen

2015

2015 International Conference on Biometrics (ICB)

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Despite being increasingly easy to acquire, 3D data is rarely used for face-based biometrics applications beyond identification. Recent work in image-based demographic biometrics has enjoyed much success, but these approaches suffer from the well-known limitations of 2D representations, particularly variations in illumination, texture, and pose, as well as a fundamental inability to describe 3D shape.This paper shows that simple 3D shape features in a face-based coordinate system are capable of representing many biometric attributes without problem-specific models or specialized domain knowledge. The same feature vector achieves impressive results for problems as diverse as age estimation, gender classification, and race classification.

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Regression Facial Attribute Classification via simultaneous dictionary learning

Moeini

Safai

et al. 2017

Pattern Recognition

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Discriminating projections for estimating face age in wild images

Cited by 5 publications

References 30 publications

Comprehensive Analysis of the Literature for Age Estimation From Facial Images

Comprehensive Analysis of the Literature for Age Estimation From Facial Images

3D face analysis for demographic biometrics

Regression Facial Attribute Classification via simultaneous dictionary learning

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