Method for estimating and modeling age and gender using facial image processing

Hayashi, Jun-ichiro; Yasumoto, M.; Ito, Hideaki; Koshimizu, Hiroyasu

doi:10.1109/vsmm.2001.969698

Cited by 48 publications

(26 citation statements)

References 7 publications

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“…The first attempts in this research line were in [72,73] where texture and shape features were jointly exploited to increase the descriptor robustness in order to estimate the human ages through a multiple-group classification scheme with 5-year intervals taking also advantage from the gender knowledge (since the aging patterns are different for males and females). Successively, the interest in appearance-based descriptors arose exponentially and LBP was used for appearance features extraction in an automatic age estimation system proposed in [74], whereas some variants were proposed and tested in [75,76].…”

Section: Agementioning

confidence: 99%

A study on different experimental configurations for age, race, and gender estimation problems

Carcagnì

Coco

Cazzato

et al. 2015

J Image Video Proc.

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This paper presents a detailed study about different algorithmic configurations for estimating soft biometric traits. In particular, a recently introduced common framework is the starting point of the study: it includes an initial facial detection, the subsequent facial traits description, the data reduction step, and the final classification step. The algorithmic configurations are featured by different descriptors and different strategies to build the training dataset and to scale the data in input to the classifier. Experimental proofs have been carried out on both publicly available datasets and image sequences specifically acquired in order to evaluate the performance even under real-world conditions, i.e., in the presence of scaling and rotation.

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Section: Agementioning

confidence: 99%

A study on different experimental configurations for age, race, and gender estimation problems

Carcagnì

Coco

Cazzato

et al. 2015

J Image Video Proc.

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show abstract

“…Wrinkles can either be transient or permanent, both capable of yielding a reliable indicator for facial expression analysis [148,149] and human age estimation [150][151][152][153][154][155][156][157]. In the literature, various automated features have been applied to represent the appearance of wrinkles, such as Gabor filter [149,150,158], Sobel filter [152][153][154], Hough transform [155], Active Contour [156] and Canny operator [148]. Also, there are other methods for detecting wrinkle segments using a watershed algorithm [151], Markov Chain Monte Carlo sampling [159], line sieving and morphological region growing [157].…”

Section: Detecting Wrinklesmentioning

confidence: 99%

Does “lie to me” lie to you? An evaluation of facial clues to high-stakes deception

Lin

Levine

2016

Computer Vision and Image Understanding

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During a forensic interview, high-stakes deception is very prevalent notwithstanding the heavy consequences that may result. Studies have shown that most untrained people cannot perform well in discerning liars and truth-tellers.Thus it has become common to adopt various technical aids to compensate for this poor judgment. Examples are polygraphs, functional Magnetic Resonance Imaging (fMRI) and linguistic analysis. However, the deception indicators used in these cases are not reliable.In the popular TV program Lie to Me, micro-expressions have been used for detecting deceit during the investigation of some criminal cases. A microexpression is considered to be a rapid and involuntary facial expression which could reveal the concealed emotion. Additionally, some psychological studies have stated that certain facial actions are more difficult to inhibit if the associated facial expressions are genuine. Similarly, these facial expressions are equally difficult to fake. This has cast light on the possibility that deception could be detected by analyzing these facial actions. However, to the best knowledge of the author, there is no computer vision research that has attempted to discriminate high-stakes deception from truth using facial expressions. Therefore, this thesis aims to test the validity of facial clues to deception detection in high-stakes situations using computer vision approaches.We note that only a limited number of the existing databases have been collected specifically for deception detection studies and none of them were obtained from real-world situations. In this thesis we present a video database of actual highstakes situations, which we have created using YouTube.We have adopted 2D appearance-based methods as the methodology to characterize the 3D facial features. Instead of building a 3D head model as is the current trend, we have extracted invariant 2D features that are related to the 3D characteristic.iii In order to discern deception and honesty, we have identified the following deceptive cues from nine separate facial regions through dynamic facial analysis: eye blink, eyebrow motion, wrinkle occurrence and mouth motion. Then these cues were integrated to form a facial behavior pattern vector. A Random Forest was trained using the collected database and applied to classify the facial patterns into deceptive and truthful categories.Despite the many uncontrolled factors (illumination, head pose and facial occlusion) in the videos in our database, we have achieved an accuracy of 76.92% when discriminating liars from truth-tellers using both micro-expressions and

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“…Texture models concentrate on extracting and manipulating intensity variations on the facial skin surface, specifically measurements of wrinkles have been used to classify ages [8]- [10], photo realistic aged faces have also been generated either by transferring wrinkles from old faces onto young faces, or via independent construction of skin models [11], [12]. The construction of 3D wrinkles has also been reported in the literature [13], [14].…”

Section: Related Workmentioning

confidence: 99%

Individualised model of facial age synthesis based on constrained regression

Bukar

Ugail

Connah

2015

2015 International Conference on Image Processing Theory, Tools and Applications (IPTA)

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-Faces convey much information. Interestingly we humans have a remarkable ability of identifying, extracting, and interpreting this information. Recently automatic facial ageing (AFA) has gained popularity due to its numerous applications which include search for missing people, biometrics, and multimedia. The problem of AFA is faced with various challenges, including incomplete training datasets, unrestrained environments, ethnic and gender variations to mention but a few. This work presents a new approach to automatic facial ageing which involves the development of a person specific facial ageing system. A color based Active Appearance Model (AAM) is used to extract facial features. Then, regression is used to model an age estimator. Age synthesis is achieved by computing a solution that minimises the distance from the original face with the use of constrained regression. The model is tested on a challenging database of single image per person. Initial results suggest that plausible images can be rerendered at different ages, automatically using the AAM representation. Using the constrained regressor we are guaranteed to get estimated ages that are exact for an individual at a given age.

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Method for estimating and modeling age and gender using facial image processing

Cited by 48 publications

References 7 publications

A study on different experimental configurations for age, race, and gender estimation problems

A study on different experimental configurations for age, race, and gender estimation problems

Does “lie to me” lie to you? An evaluation of facial clues to high-stakes deception

Individualised model of facial age synthesis based on constrained regression

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