Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Facial expression logs from long video sequences effectively provide the opportunity to analyse facial expression changes for medical diagnosis, behaviour analysis, and smart home management. Generating facial expression log involves expression recognition from each frame of a video. Hover, expression recognition performance greatly depends on the quality of the face image in the video. When a facial video is captured, it can be subjected to problems like low resolution, pose variation, low brightness, and motion blur. Thus, this paper proposes a system for constructing facial expression log by employing a face quality assessment method and investigates its influence on the representations of facial expression logs of long video sequences. A framework is defined to incorporate face quality assessment with facial expression recognition and logging system. While assessing the face quality a face-completeness metric is used along with some other state-of-the-art metrics. Instead of discarding all of the low quality faces from a video sequence, a windowing approach has been applied to select best quality faces in regular intervals. Experimental results show a good agreement beten the expression logs generated from all face frames and the expression logs generated by selecting best faces in regular intervals.
['Mohammad Ahsanul Haque', 'Kamal Nasrollahi', 'Thomas B. Moeslund']
I am writing a research paper on a idea Constructing facial expression log from video sequences using face quality assessment
[{"from": "human", "value": "I am writing a research paper on a idea Constructing facial expression log from video sequences using face quality assessment"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Facial expression logs from long video sequences effectively provide the opportunity to analyse facial expression changes for medical diagnosis, behaviour analysis, and smart home management. Generating facial expression log involves expression recognition from each frame of a video. Hover, expression recognition performance greatly depends on the quality of the face image in the video. When a facial video is captured, it can be subjected to problems like low resolution, pose variation, low brightness, and motion blur. Thus, this paper proposes a system for constructing facial expression log by employing a face quality assessment method and investigates its influence on the representations of facial expression logs of long video sequences. A framework is defined to incorporate face quality assessment with facial expression recognition and logging system. While assessing the face quality a face-completeness metric is used along with some other state-of-the-art metrics. Instead of discarding all of the low quality faces from a video sequence, a windowing approach has been applied to select best quality faces in regular intervals. Experimental results show a good agreement beten the expression logs generated from all face frames and the expression logs generated by selecting best faces in regular intervals."}]