Human age estimation using bio-inspired features

By Guo-qing Guo, Guowang Mu, Yun Fu, Thomas S. Huang. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009

632 citations recorded by the source corpus.

Subfields: Archeology, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/cfec51b23ea335bb070e6c309cb19b7a5f152759

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Citations in the corpus

Demographic Estimation from Face Images: Human vs. Machine Performance (2015, methodology): “Following the success of biologically inspired features [58] in object detection and classification [59], face recognition [60], and automatic age estimation [23], [25], [33], we choose to use BIF.”
Learning ordinal discriminative features for age estimation (2012, methodology): “Given facial features, age estimation can be converted into a multiclass classification problem [11, 9] or a regression problem [14, 30, 28].”
BiCov: a novel image representation for person re-identification and face verification (2012, background): “Based on the study of human visual system, bio-inspired features [19] have obtained excellent performances on several computer vision tasks such as object category recognition [22], face recognition [14], age estimation [11] and scene classification [23].”
Ordinal hyperplanes ranker with cost sensitivities for age estimation (2011, methodology): “In the mean absolute error (MAE) [10, 11, 12, 9, 4, 31, 26, 23, 14], large differences between the predicted and the exact ages contribute more to the total error; and in the cumulative score (CS) [10, 11, 12, 9, 4, 31, 26], the misclassification error in a tolerable range is set at zero.”
Ordinal Regression with Multiple Output CNN for Age Estimation (2016, methodology): “Guo et al . introduced many regression methods to predict the age of face image, such as SVR [15], PLS [13] and CCA [14].”
Neighborhood repulsed metric learning for kinship verification (2012, neutral): “Representative examples include face recognition [5], [8], [10], [20], [22], [27], [28], [35], [37], [40], [41], [42], [43], [54], [55], [56], [58], [64], [65], [66], [67], [68], facial expression recognition [11], [18], [70], facial age estimation [19], [21], [24], [25], [31], [39], gender classifi”
Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks (2016, background): “2008; Guo and Mu 2011), biologically-inspired models [including biologically-inspired features (BIF) (Guo et al. 2009)], compositional and dynamic models (Xu et al.”
What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics (2016, background): “[142], [149] which draws from the feed-forward path theory of the cortex for visual”
Face Recognition and Retrieval Using Cross-Age Reference Coding With Cross-Age Celebrity Dataset (2015, background): “Most existing age-related works for face image analysis focus on age estimation [17]–[19] and age simulation [20]–[22].”
Age Synthesis and Estimation via Faces: A Survey (2010, background): “[150] proposed using the Biologically Inspired”
Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval (2014, background): “Most existing age-related works for face image analysis focus on age estimation [14,15,40,25,34,7,38,6,10,22,21] and age simulation [16,30,28,29,24].”
Age , Gender and Race Estimation from Unconstrained Face Images (2014, background): “face descriptor, biologically inspired feature (BIF) descriptor [19], to extract features from “facial region with context” (Fig.”
Multi-task warped Gaussian process for personalized age estimation (2010, methodology): “Since the aging process is very complicated, some global age estimators were proposed using more sophisticated regression methods, including kernel regression [32] and support vector regression [13].”