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Deepak Ghimire,Joonwhoan Lee,Ze-Nian Li,Sunghwan Jeong,Sang Hyun Park,Hyo Sub Choi 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.3
In this paper, we present a method for fully automatic facial expression recognition in facial image sequences using feature extracted from tracking of facial landmarks. The facial landmarks at the first frame of the image sequence under examination are initialized using elastic bunch graph matching (EBGM) algorithm and tracked in the consecutive video frame over time. At first, the most discriminative geometric features in terms of triangle are selected using multi-class AdaBoost with extreme learning machine (ELM) classifier. The features for facial expression recognition (FER) are extracted from AdaBoost selected most discriminative set of triangles composed of facial landmarks. Finally, the facial expressions are recognized using support vector machines (SVM) classification. The results on the extended Cohn-Kanade (CK+) and Multimedia Understanding Group (MUG) facial expression database shows a recognition accuracy of 97.80% and 95.50% respectively using proposed facial expression recognition system.
Security Verification of Video Telephony System Implemented on the DM6446 DaVinci Processor
Ghimire, Deepak,Kim, Joon-Cheol,Lee, Joon-Whoan The Korea Contents Association 2012 International Journal of Contents Vol.8 No.1
In this paper we propose a method for verifying video in a video telephony system implemented in DM6446 DaVinci Processor. Each frame is categorized either error free frame or error frame depending on the predefined criteria. Human face is chosen as a basic means for authenticating the video frame. Skin color based algorithm is implemented for detecting the face in the video frame. The video frame is classified as error free frame if there is single face object with clear view of facial features (eyes, nose, mouth etc.) and the background of the image frame is not different then the predefined background, otherwise it will be classified as error frame. We also implemented the image histogram based NCC (Normalized Cross Correlation) comparison for video verification to speed up the system. The experimental result shows that the system is able to classify frames with 90.83% of accuracy.
Enhancement of Color Images with Blue Sky Using Different Method for Sky and Non-Sky Regions
( Deepak Ghimire ),( Suresh Raj Pant ),( Joonwhoan Lee ) 한국정보처리학회 2013 한국정보처리학회 학술대회논문집 Vol.20 No.1
In this paper, we proposed a method for enhancement of color images with sky regions. The input image is converted into HSV space and then sky and non-sky regions are separated. For sky region, saturation enhancement is performed for each pixel based on the enhancement factor calculated from the average saturation of its local neighborhood. On the other hand, for the non-sky region, the enhancement is applied only on the luminance value (V) component of the HSV color image, which is performed in two steps. The luminance enhancement, which is also called as dynamic range compression, is carried out using nonlinear transfer function. Again, each pixel is further enhanced for the adjustment of the image contrast depending upon the center pixel and its neighborhood pixel values. At last, the original H and V component image and enhanced S component image for the sky region, and original H and S component image and enhanced V component image for the non-sky region are converted back to RGB image.
Extreme Learning Machine Ensemble Using Bagging for Facial Expression Recognition
Ghimire, Deepak,Lee, Joonwhoan Korea Information Processing Society 2014 Journal of information processing systems Vol.10 No.3
An extreme learning machine (ELM) is a recently proposed learning algorithm for a single-layer feed forward neural network. In this paper we studied the ensemble of ELM by using a bagging algorithm for facial expression recognition (FER). Facial expression analysis is widely used in the behavior interpretation of emotions, for cognitive science, and social interactions. This paper presents a method for FER based on the histogram of orientation gradient (HOG) features using an ELM ensemble. First, the HOG features were extracted from the face image by dividing it into a number of small cells. A bagging algorithm was then used to construct many different bags of training data and each of them was trained by using separate ELMs. To recognize the expression of the input face image, HOG features were fed to each trained ELM and the results were combined by using a majority voting scheme. The ELM ensemble using bagging improves the generalized capability of the network significantly. The two available datasets (JAFFE and CK+) of facial expressions were used to evaluate the performance of the proposed classification system. Even the performance of individual ELM was smaller and the ELM ensemble using a bagging algorithm improved the recognition performance significantly.
Infrared Sensitive Camera Based Finger-Friendly Interactive Display System
Ghimire, Deepak,Kim, Joon-Cheol,Lee, Kwang-Jae,Lee, Joon-Whoan The Korea Contents Association 2010 International Journal of Contents Vol.6 No.4
In this paper we present a system that enables the user to interact with large display system even without touching the screen. With two infrared sensitive cameras mounted on the bottom left and bottom right of the display system pointing upwards, the user fingertip position on the selected region of interest of each camera view is found using vertical intensity profile of the background subtracted image. The position of the finger in two images of left and right camera is mapped to the display screen coordinate by using pre-determined matrices, which are calculated by interpolating samples of user finger position on the images taken by pointing finger over some known coordinate position of the display system. The screen is then manipulated according to the calculated position and depth of the fingertip with respect to the display system. Experimental results demonstrate an efficient, robust and stable human computer interaction.
Extreme Learning Machine Ensemble Using Bagging for Facial Expression Recognition
( Deepak Ghimire ),( Joon Whoan Lee ) 한국정보처리학회 2014 Journal of information processing systems Vol.10 No.3
An extreme learning machine (ELM) is a recently proposed learning algorithm for a single-layer feed forward neural network. In this paper we studied the ensemble of ELM by using a bagging algorithm for facial expression recognition (FER). Facial expression analysis is widely used in the behavior interpretation of emotions, for cognitive science, and social interactions. This paper presents a method for FER based on the histogram of orientation gradient (HOG) features using an ELM ensemble. First, the HOG features were extracted from the face image by dividing it into a number of small cells. A bagging algorithm was then used to construct many different bags of training data and each of them was trained by using separate ELMs. To recognize the expression of the input face image, HOG features were fed to each trained ELM and the results were combined by using a majority voting scheme. The ELM ensemble using bagging improves the generalized capability of the network significantly. The two available datasets (JAFFE and CK+) of facial expressions were used to evaluate the performance of the proposed classification system. Even the performance of individual ELM was smaller and the ELM ensemble using a bagging algorithm improved the recognition performance significantly.
A Robust Face Detection Method Based on Skin Color and Edges
Ghimire, Deepak,Lee, Joonwhoan Korea Information Processing Society 2013 Journal of information processing systems Vol.9 No.1
In this paper we propose a method to detect human faces in color images. Many existing systems use a window-based classifier that scans the entire image for the presence of the human face and such systems suffers from scale variation, pose variation, illumination changes, etc. Here, we propose a lighting insensitive face detection method based upon the edge and skin tone information of the input color image. First, image enhancement is performed, especially if the image is acquired from an unconstrained illumination condition. Next, skin segmentation in YCbCr and RGB space is conducted. The result of skin segmentation is refined using the skin tone percentage index method. The edges of the input image are combined with the skin tone image to separate all non-face regions from candidate faces. Candidate verification using primitive shape features of the face is applied to decide which of the candidate regions corresponds to a face. The advantage of the proposed method is that it can detect faces that are of different sizes, in different poses, and that are making different expressions under unconstrained illumination conditions.