Robust gender and age estimation under varying facial pose

Takimoto, Hironori; Mitsukura, Yasue; Fukumi, Minoru; Akamatsu, Norio

doi:10.1002/ecj.10125

Cited by 24 publications

(17 citation statements)

References 4 publications

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“…Thus, Takimoto et al [22] extracted the features around the eyes and mouth to bypass the complexity of different facial component placements. Motivated by psychological experiments [27] which prove that individual facial components can indicate the gender, we decided to use facial components rather than the whole face.…”

Section: Combination Of Facial Component Classifiersmentioning

confidence: 99%

“…Takimoto et al [22] used local information to facilitate feature extraction around the eyes and mouth, which requires the positions of eyes and mouth to be exactly located in advance. Lian and Lu [6] aligned the facial images based on the position of eyes and applied Local Binary Pattern (LBP) [23] to feature extraction and SVM to gender classification directly.…”

Section: Related Workmentioning

confidence: 99%

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Multi-view gender classification using symmetry of facial images

Lian

2011

Neural Comput & Applic

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In this paper, we propose a multi-view gender classification system with a hierarchical framework using facial images as input. The front end of the framework is a classifier, which will properly divides the input images into several groups. To ease the data sparsity problem in the multi-view scenario, facial symmetry is used to reduce the number of views. Moreover, we adopt soft assignment when dividing the input data, which can reduce the errors caused by the boundary effect in hard assignment. Then for each group, we train a gender classifier, called an expert. These experts can be any commonly used classifiers, such as support vector machines or neural networks. In this step, facial components instead of the whole face are used to achieve higher robustness against variations caused by facial alignment, illumination and occlusions. Experimental results demonstrate that our framework significantly improves the performance.

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Section: Combination Of Facial Component Classifiersmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Multi-view gender classification using symmetry of facial images

Lian

2011

Neural Comput & Applic

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“…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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“…Some features [1,2,3,4] are primarily applied to model the skin texture changes and the fine wrinkles on the face, while others [5,6,7,8,9] are mainly used to capture the pronounced wrinkles of the face. To measure the effectiveness of these features, they are used in age prediction pipelines.…”

Section: Introductionmentioning

confidence: 99%

Age estimation under changes in image quality: An experimental study

Alnajar

Gevers

Karaoğlu

2015

2015 IEEE International Conference on Image Processing (ICIP)

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In this paper, we investigate the influence of image quality on the performance of aging features. Age estimation systems used or designed a number of aging features to capture the aging cues from the face such as skin texture and wrinkles. These aging cues are sensitive to small changes in the imaging conditions which suggests considering the imaging quality when extracting such information.Although interesting performances are reported on various datasets, the effect of image quality has not been addressed. We introduce a scheme to explore the influence of image quality on the performance of appearance aging features. A number of datasets are experimented on where artifacts resulted from different types of noise are considered. Finally, we propose a method to automatically apply the most suitable features based on the quality of the image. The results show that better or comparable performance is obtained when automatically applying different features, based on image quality, in comparison to a single (best) feature type.

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Robust gender and age estimation under varying facial pose

Cited by 24 publications

References 4 publications

Multi-view gender classification using symmetry of facial images

Multi-view gender classification using symmetry of facial images

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

Age estimation under changes in image quality: An experimental study

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