Pain Recognition Using Artificial Neural Network

Monwar, Md. Maruf; Rezaei, Siamak

doi:10.1109/isspit.2006.270764

Cited by 31 publications

(22 citation statements)

References 3 publications

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“…Here, Eigeneyes and Eigenlips are also used for better classification result [29]. Sometimes artificial neural network-based back propagation algorithms showed very good result to distinguish between pain versus no pain from facial features [13], [30]. A Bayesian extension of SVM named relevance vector machine (RVM) is also used [31] to make high classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

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Pain Level Detection From Facial Image Captured by Smartphone

Hasan

Ahsan

Ahamed

et al. 2016

Journal of Information Processing

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Accurate symptom of cancer patient in regular basis is highly concern to the medical service provider for clinical decision making such as adjustment of medication. Since patients have limitations to provide self-reported symptoms, we have investigated how mobile phone application can play the vital role to help the patients in this case. We have used facial images captured by smart phone to detect pain level accurately. In this pain detection process, existing algorithms and infrastructure are used for cancer patients to make cost low and user-friendly. The pain management solution is the first mobile-based study as far as we found today. The proposed algorithm has been used to classify faces, which is represented as a weighted combination of Eigenfaces. Here, angular distance, and support vector machines (SVMs) are used for the classification system. In this study, longitudinal data was collected for six months in Bangladesh. Again, cross-sectional pain images were collected from three different countries: Bangladesh, Nepal and the United States. In this study, we found that personalized model for pain assessment performs better for automatic pain assessment. We also got that the training set should contain varying levels of pain in each group: low, medium and high.

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Section: Related Workmentioning

confidence: 99%

“…In the previous time, many research works were accomplished to measure pain from facial expressions using principal component analysis (PCA) [13]. In those case, the accuracy level was found low.…”

Section: Introductionmentioning

confidence: 99%

Pain Level Detection From Facial Image Captured by Smartphone

Hasan

Ahsan

Ahamed

et al. 2016

Journal of Information Processing

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“…Their system achieves a generalized recognition rate of80%. Monwar & Rezaei [15] use location and shape features to represent the pain information. These features are used as inputs to the standard back-propagation in the form of a three-layer neural network with one hidden layer for www.ijacsa.thesai.org classificationof painful and painless faces.…”

Section: Hybrid-based Approachesmentioning

confidence: 99%

Automatic Facial Expression Recognition Based on Hybrid Approach

Bermani¹,

Ghalwash²,

Youssif³

2012

IJACSA

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Abstract-The topic of automatic recognition of facial expressions deduce a lot of researchers in the late last century and has increased a great interest in the past few years. Several techniques have emerged in order to improve the efficiency of the recognition by addressing problems in face detection and extraction features in recognizing expressions. This paper has proposed automatic system for facial expression recognition which consists of hybrid approach in feature extraction phase which represent a combination between holistic and analytic approaches by extract 307 facial expression features (19 features by geometric, 288 feature by appearance). Expressions recognition is performed by using radial basis function (RBF) based on artificial neural network to recognize the six basic emotions (anger, fear, disgust, happiness, surprise, sadness) in addition to the natural.The system achieved recognition rate 97.08% when applying on person-dependent database and 93.98% when applying on person-independent.

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“…A Bayesian extension of SVM named Relevance Vector Machine (RVM) has been adopted in [21] to increase classification accuracy. Several papers [22,23] relied on artificial neural network based back propagation algorithm to find classification decision from extracted facial features. Many other researchers including Brahnam et al [24,25], Pantic et al [7,8] worked in the area of automatic facial expression detection.…”

Section: Introductionmentioning

confidence: 99%

IRENE: Context aware mood sharing for social network

Haque

Adibuzzaman

Polyak

et al. 2011

2011 IEEE International Conference on Service-Oriented Computing and Applications (SOCA)

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Social networking sites like Facebook, twitter, and myspace are becoming overwhel mingly powerful media in today's world. Facebook has 500 million active users and twitter has 190 million visitors per month and increasing each second. On the other hand number of smart phone users has crossed 45 millions. Now we are focusing on building an application that will connect these two revolutionary spheres of modern science that has huge potential in different sectors. IRENE a facial expression based mood detection model has been developed by capturing images while the users use webcam supported laptops or mobile phones. This image will be analyzed to classify one of several moods. This mood information will be shared in the user profile of Facebook according to privacy settings of the user. Several activities and events will also be generated based on the identified mood.

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Pain Recognition Using Artificial Neural Network

Cited by 31 publications

References 3 publications

Pain Level Detection From Facial Image Captured by Smartphone

Pain Level Detection From Facial Image Captured by Smartphone

Automatic Facial Expression Recognition Based on Hybrid Approach

IRENE: Context aware mood sharing for social network

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