Priska Choirina
@uniramalang.ac.id
Science and Technology
Universitas Islam Raden Rahmat
RESEARCH INTERESTS
Computer Vision, Facial Emotion
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Ulla Rosiani, , Priska Choirina, Niyalatul Muna, Eko Mulyanto, Surya Sumpeno, Mauridhi Purnomo, , , ,et al.
The Intelligent Networks and Systems Society
Micro-expression is an expression when a person tries to held or hidden, but the leak of this emotion still occurs in one or two areas of the face or maybe a short expression that across in the whole-face. Not more than 500ms, micro-expressions can be difficult to recognize and detect where the leakage area is located. This study presents a new method to recognize and detect the subtle motion on the facial components area using Phase Only Correlation algorithm with All Block Search (POC-ABS) to estimate the motion of all block areas. This block matching method is proposed by comparing each block in the two frames to determine whether there is movement or not. If the two blocks are identical, then the motion vector value is not displayed, whereas if the blocks are non-identical, the motion vector value of the POC is displayed. The motion vector, which is as a motion feature, estimates whether or not there are movements in the same block. In order to further confirm the reliability of the proposed method, two different classifiers were used for the micro-expression recognition of the CASME II dataset. The highest performance results are for SVM at 94.3 percent and for KNN at 95.6 percent. Finally, this algorithm detects leaks of motion based on the ratio of the motion vectors. The left and right eyebrows are dominant when expressing disgust, sadness, and surprise. Meanwhile, the movements of the right eye and left eye were the most dominant when the happiness expression.
R A Asmara, P Choirina, C Rahmad, A Setiawan, F Rahutomo, R D R Yusron, and U D Rosiani
IOP Publishing
Abstract Micro-expression recognition is one of the popular researches in analysing expressions on the face. Micro-expression is a facial movement that occurs in a short time and is difficult to identify manually by human eyes. In general research, facial landmarks are used to form a large size ROI for each facial feature for the feature extraction process. In this study, we track the subtle motions of micro expressions by using point features. This approach aims to get feature extraction from tracking results and then analyse micro-expression. We compared the Active Shape Model and Response Map Fitting methods to produce accurate points and fast time on facial features. To measure the subtle motion tracking of facial features in each frame tracking is done using the Kanade-Lucas-Tomasi method. To estimate the rationality of our method, we conducted an experiment on CASME II and SAMM dataset for micro-expressions. The results show that the points on DRMF are more accurate with point-to-point error is 7.9 and the time taken is faster which requires time is 0.02 second. We evaluated the method proposed for evaluation showed that using CASME II - Naive Bayes (79.3%) and SAMM - Naive Bayes (74.6%).
R A Asmara, R D R Yusron, F Rahutomo, R Ariyanto, D K P Aji, and P Choirina
IOP Publishing
Abstract Algorithms developed to identify people with iris image data have been tested in many field and laboratory experiment. This paper analysis some a parameters of iris image used to recognize human. Iris recognition system, which is applied based on segmentation, normalization, encoding, and matching is also describe in this paper. Circle Hough Transform segmentation module used to find the inner and outer boundaries of the iris. The experiment was carried out using CASIA v1 iris database with grayscale images. Shape, intensity, and location information for localizing the pupil or iris and normalizing the iris area a used iris segmentation by unwrapping circular area into a rectangular area. Normalized area will be used to extract the features using Gray Level Co-occurrence Matrix (GLCM) and Gabor filter, the feature compared the recognition accuracy using Support Vector Machines (SVM) and Naive Bayes classifiers. GLCM feature test results achieved 95.24% SVM classification accuracy, whereas using achieved 85.71% Naive Bayes. Gabor feature test results achieved 95.24% SVM classification accuracy, whereas using achieved 95.23% Naive Bayes. The classification process based on GLCM and Gabor features show that the SVM method have to highest recognition accuracy compare to Naive Bayes classifier.
Ulla Delfana Rosiani, Ariadi Retno Tri Hayati Ririd, Priska Choirina, Adri Gabriel Sooai, Surya Sumpeno, and Mauridhi Hery Purnomo
IEEE
In the micro-expression detection system on part of facial components, it is necessary to detect that component in accurately, precisely and fast. This study shows a comparison between accuracy and speed in the detection of the component area of the face (right eye, left eye and mouth) automatically. Micro expression occurs in a short time, fast and in very smooth movements. The face component detection method conducted in this study is a feature-based method (Viola Jones method) which is compared with the model-based method (ASM and DRMF methods). Comparison of the accuracy of the three methods in the detection of the face component area shows that the ASM and DRMF methods provide an accuracy value of 100%, while the Haar Cascade Classifier method shows an average accuracy of 44%. Meanwhile, on speed measurement to find the face component area, the DRMF method is the fastest with an average processing time of 0.08 seconds followed by ASM method of 0.14 seconds, and the Haar method of 0.25 seconds.