Deep Transformation Learning for Depression Diagnosis from Facial Images

2017 ◽  
pp. 13-22 ◽  
Author(s):  
Yajun Kang ◽  
Xiao Jiang ◽  
Ye Yin ◽  
Yuanyuan Shang ◽  
Xiuzhuang Zhou
Keyword(s):  
Depression Diagnosis ◽  
Transformation Learning ◽  
Facial Images

FACE RECOGNITION USING FUZZY INFERENCE SYSTEM

2009 ◽  
Vol 8 (3) ◽  
pp. 887-897
Author(s):  
Vishal Paika ◽  
Er. Pankaj Bhambri
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Facial Features ◽  
Facial Appearance ◽  
Feature Points ◽  
Inference System ◽  
Detection Techniques ◽  
The Face ◽  
Hair Style ◽  
Facial Images

The face is the feature which distinguishes a person. Facial appearance is vital for human recognition. It has certain features like forehead, skin, eyes, ears, nose, cheeks, mouth, lip, teeth etc which helps us, humans, to recognize a particular face from millions of faces even after a large span of time and despite large changes in their appearance due to ageing, expression, viewing conditions and distractions such as disfigurement of face, scars, beard or hair style. A face is not merely a set of facial features but is rather but is rather something meaningful in its form.In this paper, depending on the various facial features, a system is designed to recognize them. To reveal the outline of the face, eyes, ears, nose, teeth etc different edge detection techniques have been used. These features are extracted in the term of distance between important feature points. The feature set obtained is then normalized and are feed to artificial neural networks so as to train them for reorganization of facial images.

Expectations about pain and analgesic treatment depend on medical providers’ facial appearances

2020 ◽  
Author(s):  
Elizabeth A. Necka ◽  
Carolyn Amir ◽  
Troy C. Dildine ◽  
Lauren Yvette Atlas
Keyword(s):  
Patient Outcomes ◽  
Visual Information ◽  
Healthcare Providers ◽  
Medical Procedures ◽  
Medical Providers ◽  
Analgesic Treatment ◽  
Digital Services ◽  
Receive Treatment ◽  
Facial Images ◽  
Trait Impressions

There is a robust link between patients’ expectations and clinical outcomes, as evidenced by the placebo effect. These expectations are shaped by the context surrounding treatment, including the patient-provider interaction. Prior work indicates that the provider’s behavior and characteristics, including warmth and competence, can shape patient outcomes. Yet humans rapidly form trait impressions of others prior to any in-person interaction. Here, we tested whether trait-impressions of hypothetical medical providers, based purely on facial images, influence participants’ choice of medical providers and expectations about their health following hypothetical medical procedures performed by those providers in a series of vignettes. Across five studies, participants selected providers who appeared more competent, based on facial visual information alone. Further, providers’ apparent competence predicted participants’ expectations about post-procedural pain and medication use. Participants’ perception of their similarity to providers also shaped expectations about pain and treatment outcomes. Our results suggest that humans develop expectations about their health outcomes prior to even setting foot in the clinic, based exclusively on first impressions. These findings have strong implications for health care, as individuals increasingly rely on digital services to choose healthcare providers, schedule appointments, and even receive treatment and care, a trend which is exacerbated as the world embraces telemedicine.

scholarly journals Gender Differences in Depression and Sex Hormones among Patients Receiving Long-Term Opioid Treatment for Chronic Noncancer Pain in Taiwan—A Multicenter Cross-Sectional Study

2021 ◽  
Vol 18 (15) ◽  
pp. 7837
Author(s):  
Shung-Tai Ho ◽  
Tso-Chou Lin ◽  
Chun-Chang Yeh ◽  
Kuang-I Cheng ◽  
Wei-Zen Sun ◽  
...  
Keyword(s):  
Sexual Function ◽  
Sex Hormones ◽  
Sex Hormone ◽  
Cross Sectional ◽  
Chronic Noncancer Pain ◽  
Depression Diagnosis ◽  
Opioid Treatment ◽  
Hormone Levels ◽  
Noncancer Pain

Background: Long-term use of opioids for chronic noncancer pain is associated with sex hormone disturbances. The interferences with sex hormones, sexual function, and depression were investigated in patients with chronic noncancer pain. Methods: A cross-sectional multicenter survey was conducted on 170 officially registered outpatients receiving long-term opioid treatment in nine medical centers in Taiwan between October 2018 and July 2019. Serum sex hormone levels were examined after the collection of self-administered questionnaires containing the Taiwanese version of the Brief Pain Inventory, depressive status, and sexual function interference. Results: Among 117 (68.8%) questionnaire responses from 170 enrolled outpatients, 38 women and 62 men completed the sex hormone tests, among whom only 23 (23%) had previously received blood hormone tests. Low serum total testosterone levels were detected in 34 (89.5%) women (<30 ng/dL) and 31 (50%) men (<300 ng/dL). Over 60% of women and men reported reduced sexual desire and function despite a nearly 50% reduction in pain intensity and daily function interference over the previous week after opioid treatment. Women generally had higher risks of a depression diagnosis (p = 0.034) and severe depressive symptoms (p = 0.003) and nonsignificantly lower opioid treatment duration (median 81 vs. 120 months) and morphine milligram equivalent (median 134 vs. 165 mg/day) compared with men. Conclusions: This survey demonstrated the high prevalence of depression diagnosis, low sex hormone levels, and reduced sexual function among Taiwanese patients with chronic noncancer pain receiving prolonged opioid therapy. Regular hypogonadal screenings are recommended for further management.

Multifactorial prediction of depression diagnosis and symptom dimensions

2021 ◽  
Vol 298 ◽  
pp. 113805
Author(s):  
Mary E. McNamara ◽  
Jason Shumake ◽  
Rochelle A. Stewart ◽  
Jocelyn Labrada ◽  
Alexandra Alario ◽  
...  
Keyword(s):  
Depression Diagnosis ◽  
Symptom Dimensions

scholarly journals Recognizing Human Races through Machine Learning—A Multi-Network, Multi-Features Study

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 195
Author(s):  
Adrian Sergiu Darabant ◽  
Diana Borza ◽  
Radu Danescu
Keyword(s):  
Race And Ethnicity ◽  
Large Scale ◽  
Recognition Rate ◽  
Social Signals ◽  
Image Characteristics ◽  
Race Effect ◽  
Race Ethnicity ◽  
The Impact ◽  
Facial Images ◽  
Ethnicity Classification

The human face holds a privileged position in multi-disciplinary research as it conveys much information—demographical attributes (age, race, gender, ethnicity), social signals, emotion expression, and so forth. Studies have shown that due to the distribution of ethnicity/race in training datasets, biometric algorithms suffer from “cross race effect”—their performance is better on subjects closer to the “country of origin” of the algorithm. The contributions of this paper are two-fold: (a) first, we gathered, annotated and made public a large-scale database of (over 175,000) facial images by automatically crawling the Internet for celebrities’ images belonging to various ethnicity/races, and (b) we trained and compared four state of the art convolutional neural networks on the problem of race and ethnicity classification. To the best of our knowledge, this is the largest, data-balanced, publicly-available face database annotated with race and ethnicity information. We also studied the impact of various face traits and image characteristics on the race/ethnicity deep learning classification methods and compared the obtained results with the ones extracted from psychological studies and anthropomorphic studies. Extensive tests were performed in order to determine the facial features to which the networks are sensitive to. These tests and a recognition rate of 96.64% on the problem of human race classification demonstrate the effectiveness of the proposed solution.

Sign in / Sign up

Export Citation Format

Share Document