Personalized facial expression recognition in indoor environments

Chang, Chuan‐Yu; Huang, Yan‐Chiang

doi:10.1109/ijcnn.2010.5596316

Cited by 20 publications

(14 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, expressions are classified in one of 7 categories (neutral, happy, surprised, fearful, angry, sad, disgusted), as proposed by Ekman [13]. Expressing emotions is a highly individual task, which challenges automated expression recognition systems significantly [14]. At the same time, human emotion-expression is a key factor in human interaction.…”

Section: B Related Workmentioning

confidence: 99%

“…Image-based approaches on the other hand are holistic, hence features are extracted from the whole image, which allows for fast and simple computation, at the price of high dimensionality [14]. Apart from feature extraction, feature classification is a key ingredient in expression recognition.…”

Section: B Related Workmentioning

confidence: 99%

“…Apart from feature extraction, feature classification is a key ingredient in expression recognition. Prominent classifiers in this context include SVM [16], [17], [18], as well as radial-basis function neural networks (RBFNN) [14].…”

Section: B Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Expression recognition for severely demented patients in music reminiscence-therapy

Dantcheva¹,

Biliński²,

Nguyen³

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

Abstract-Recognizing expressions in severely demented Alzheimer's disease (AD) patients is essential, since such patients have lost a substantial amount of their cognitive capacity, and some even their verbal communication ability (e.g., aphasia). This leaves patients dependent on clinical staff to assess their verbal and non-verbal language, in order to communicate important messages, as of the discomfort associated to potential complications of the AD. Such assessment classically requires the patients' presence in a clinic, and time consuming examination involving medical personnel. Thus, expression monitoring is costly and logistically inconvenient for patients and clinical staff, which hinders among others large-scale monitoring. In this work we present a novel approach for automated recognition of facial activities and expressions of severely demented patients, where we distinguish between four activity and expression states, namely talking, singing, neutral and smiling. Our approach caters to the challenging setting of current medical recordings of musictherapy sessions, which include continuous pose variations, occlusions, camera-movements, camera-artifacts, as well as changing illumination. Additionally and importantly, the (elderly) patients exhibit generally less profound facial activities and expressions in a range of intensities and predominantly occurring in combinations (e.g., talking and smiling). Our proposed approach is based on the extension of the Improved Fisher Vectors (IFV) for videos, representing a video-sequence using both, local, as well as the related spatio-temporal features. We test our algorithm on a dataset of over 229 video sequences, acquired from 10 AD patients, with promising results, which have sparked substantial interest in the medical community. The proposed approach can play a key role in assessment of different therapy treatments, as well as in remote large-scale healthcare-frameworks.

show abstract

Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Expression recognition for severely demented patients in music reminiscence-therapy

Dantcheva¹,

Biliński²,

Nguyen³

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

show abstract

“…In the frontal face image, there are 17 facial feature points detected beforehand using empirical information in YCbCr and HSV color spaces [5][6]. Fig.…”

Section: B Facial Features Detectionmentioning

confidence: 99%

Automatic Facial Skin Defects Detection and Recognition System

Chang

Liao

2011

2011 Fifth International Conference on Genetic and Evolutionary Computing

Self Cite

View full text Add to dashboard Cite

Recently medical cosmetology has attracted significant business opportunity. Micro cosmetic surgery usually involves the minimally invasive cosmetic procedures such as non-ablative laser procedure for skin rejuvenation. However, an appropriate treatment selection for skin relies on accurate preoperative evaluation. In this paper, an automatic facial skin defects detection and recognition system is proposed. The system automatic locates the facial region and extracts region of interest. A support-vector-machine-based classifier is then used to classify the potential defects into spot, acne and normal skin. Experimental results demonstrate effectiveness of the proposed approach.

show abstract

“…In the facial features, there are 12 facial feature points that were detected beforehand using empirical information in YCbCr and HSV color space [11]. However, the facial features do not include nose.…”

Section: B Facial Feature Extractionmentioning

confidence: 99%

Automatic Facial Skin Defect Detection System

Chang

Chung

et al. 2010

2010 International Conference on Broadband, Wireless Computing, Communication and Applications

Self Cite

View full text Add to dashboard Cite

Abstract²Skin analysis is one of the most important procedures before medical cosmetology. Most conventional skin analysis systems are semi-automatic. They often require human intervention. In this study, an automatic facial skin defect detection approach is proposed. The system first detects human face in the facial image. Based on the detected face, facial features are extracted to locate regions of interest. Then, a pattern recognition approach is applied to detect facial skin defects, such as spots and wrinkles, in the regions of interest. For a specific kind of defect, a classifier is designed to provide higher performance for recognition. Using few features extracted from the region of interest, the proposed approach can successfully detect the skin defects. Experimental results demonstrate effectiveness of the proposed approach.

show abstract

Personalized facial expression recognition in indoor environments

Cited by 20 publications

References 19 publications

Expression recognition for severely demented patients in music reminiscence-therapy

Expression recognition for severely demented patients in music reminiscence-therapy

Automatic Facial Skin Defects Detection and Recognition System

Automatic Facial Skin Defect Detection System

Contact Info

Product

Resources

About