Automatic Age Estimation Based on Facial Aging Patterns

By Xin Geng, Zhi-Hua Zhou, K. Smith‐Miles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007

1,035 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Dermatology, Marketing

View Paper: https://www.semanticscholar.org/paper/c7584dee6cc077d74b7126e030b68e266fbe11b1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Xiaogang Wang
3. Dacheng Tao
4. Yu Qiao
5. Chen Change Loy
6. Xiaoou Tang
7. Ming-Hsuan Yang
8. Yi Yang

In Dermatology

1. Xiaoming Liu
2. Yu Zhang
3. Unsang Park
4. Hu Han
5. Anil K. Jain
6. Yi-Ping Hung
7. Yun Fu
8. Arun A. Ross

In Marketing

1. Unsang Park
2. Hu Han
3. Anil K. Jain
4. Yi-Ping Hung
5. Yun Fu
6. Arun A. Ross
7. Thomas S. Huang
8. Petros Elia

Citations in the corpus

• Face Recognition Vendor Test (FRVT) - Performance of Automated Age Estimation Algorithms (2014, background): “A number of factors including the variation between ethnicities and gender, as well as dependence on external factors such as health conditions and lifestyle [9], have introduced challenges to age estimation.”
• Ordinal Regression with Multiple Output CNN for Age Estimation (2016, methodology): “Due to that geometry and texture features are helpful to distinguish baby, adult, and senior, many methods were proposed based on the AAM model [8] since it is a natural tool to simultaneously model the shape and texture of facial images [19][12].”
• Multi-task warped Gaussian process for personalized age estimation (2010, methodology): “In [10, 9], a personalized age estimator was proposed by modeling the problem as a time series problem.”
• Cumulative Attribute Space for Age and Crowd Density Estimation (2013, methodology): “Age estimation –Most existing techniques for age estimation from facial images fall into three categories: multiclass classification [13], regression [16], and hybrid [15] of the two, with regression models being the most widely used.”
• 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”
• What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics (2016, methodology): “This approach was tested [147], [148] on the FG-NET face database, resulting in a reported MAE of 6.”
• External validity of sentiment mining reports: Can current methods identify demographic biases, event biases, and manipulation of reviews? (2014, neutral): “The list of papers found is here: [12,14–17,26,28,31–35,43–60].”
• Age and gender classification using convolutional neural networks (2015, methodology): “On a different line of work are methods that represent the aging process as a subspace [16] or a manifold [19].”
• Face Recognition and Retrieval Using Cross-Age Reference Coding With Cross-Age Celebrity Dataset (2015, methodology): “One of the approaches is to construct 2D or 3D aging models [22]–[24] to reduce the age variation in face matching.”
• ChaLearn Looking at People and Faces of the World: Face AnalysisWorkshop and Challenge 2016 (2016, methodology): “This dataset, widely used in the fields of face recognition and age estimation [21] is extremely skewed.”
• Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks (2016, neutral): “We refer to [42, 20, 14, 26, 2, 9] for broader literature reviews.”
• Demographic Estimation from Face Images: Human vs. Machine Performance (2015, methodology): “10 we show the Cumulative Score (CS) [19] of age estimation by human and the proposed approach within 0-10 years absolute error.”
• Human age estimation using bio-inspired features (2009, methodology): “Thus face aging is uncontrollable and personalized [24] [7] [11].”
• Age-Invariant Face Recognition (2010, methodology): “Following [5], we define the aging pattern as an array of face models from a single subject indexed by her age.”
• Learning ordinal discriminative features for age estimation (2012, methodology): “In recent years, human age estimation attracted much attention in the communities of computer vision and pattern recognition due to its potential applications in softbiometrics [8], human-computer interaction (HCI) [11], security control [8], surveillance monitoring [24], and electronic customer rel”
• Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach (2017, neutral): “8 years [35] to 2.”
• Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval (2014, methodology): “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 Synthesis and Estimation via Faces: A Survey (2010, methodology): “In terms of utilizing the AAMs for encoding face images, Geng et al. [131], [37] used 200 AAMs features to encode each face image, while only 50 features were used in [17].”