Emotion recognition in computer vision by using machine learning technique in static images
| dc.contributor.author | Amna Arshad | |
| dc.date.accessioned | 2025-12-05T16:43:50Z | |
| dc.date.available | 2025-12-05T16:43:50Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | An immense area of technology which is covering many area and huge part of discussion is a computer vision this area cover many techniques and many AI facts so that this area is a widely covered and used by different authors and many working areas. Computer vision is based on many techniques which has deep learning techniques which hits many areas so that computer vision area is also known as emotions recognition. Emotions recognition is a wide area of technology which is based on behavioral changes, environmental changes. Emotions recognition from static images is a bit challenging task. The modalities include in this task are static images which are having different type of emotions as emotions are intense part of behavior because these are nonverbal part of communication and this part has a key role to describing the behavioral changes, these changes are effect on a person by any situation. In recent few years when machine learning and AI become popular and authors has work on it than emotions recognition become very continuous discussion part of technology because these are covered by many areas of technology so that emotions in static images are very important in many facts and these facts are based on behavioral changes and these changes are the factors of emotions. In this study there are seven main and basic emotions type will be discussed which are (Natural, Happy, Sad, Surprise, Fear, Anger and Disgust) these emotions types are basic types which are also discussed by many authors although emotions are very vast area of image recognitions because these are recognized by the face expressions which tends to be emotions. So that these are behavioral phenomena which are based on nature environment and situations which effects on human. According to the artificial intelligence emotional study is based on different techniques and areas which are interlined with each other whereas those area which are connected are neural network iv which are used to describes specificity, image processing area which indicates the different area of images and emotional static images are one of them so that other part which is that part of techniques and that is based on the machine learning area of techniques which has an important part to complete and describes all that parts in a legitimate way. So that to describe accuracy rate in images in this study here are some techniques which can describes the clearly the emotional states. Techniques which are used are show that the accuracy of emotional states of images which is 98%. So, that Here this study is based on the emotions so that CNN (Convolutional Neural Network) has a major role to describes different part of emotions in static images those static images are taken from the well-known data bases ck+ (Cohan-Kanade) and JAFFE (Japanese female facial expression) both of these data bases are based on static images which are having seven different type of emotions. JAFFE and CK+ has emotional state images whereas these are using for describing the accuracy rate in the images. | |
| dc.identifier.uri | https://escholar.umt.edu.pk/handle/123456789/15255 | |
| dc.language.iso | en | |
| dc.publisher | UMT Lahore | |
| dc.title | Emotion recognition in computer vision by using machine learning technique in static images | |
| dc.type | Thesis |
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