112 research outputs found

    EXPERIMENTAL STUDY ON LIP AND SMILE DETECTION

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    This paper presents a lip and smile detection method based-on the normalized RGB chromaticity diagram. The method employs the popular Viola-Jones detection method to detect the face. To avoid the false positive, the eye detector is introduced in the detection stage. Only the face candidates with the detected eyes are considered as the face. Once the face is detected, the lip region is localized using the simple geometric rule. Further, the the red color thresholding based-on the normalized RGB chromaticity diagram is proposed to extract the lip. The projection technique is employed for detecting the smile state. From the experiment results, the proposed method achieves the lip detection rate of 97% and the smile detection rate of 94%. Paper ini menyajikan medote pendeteksi bibir dan senyum berdasarkan diagram tingkat kromatis RGB ternormalisasi. Metode ini menggunakan metode Viola-Jones yang populer untuk mendeteksi wajah. Untuk menghindari kesalahan positif, detektor mata diperkenalkan pada tahapan deteksi. Hanya kandidat wajah dengan mata yang telah terdeteksi yang dianggap sebagai wajah. Setelah wajah dideteksi, bagian bibir ditempatkan dengan menggunakan aturan geometris sederhana. Selanjutnya, batasan warna merah berdasarkan pada diagram kromatisitas RGB ternormalisasi digunakan untuk mengekstrak bibir. Teknik proyeksi digunakan untuk mendeteksi keadaan tersenyum. Dari hasil percobaan, metode yang diusulkan mencapai 97% untuk tingkat deteksi bibir dan 94% untuk tingkat deteksi senyum

    A VISION-BASED SYSTEM FOR MONITORING DRIVER FATIGUE

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    Abstract This paper presents a vision-based system for monitoring driver fatigue. The system is divided into three stages: face detection, eye detection, and fatigue detection. Face detection based on the skin color segmentation is used to localize face image from a whole image. To overcome the normalized RGB chromaticity diagram is adopted. After face is localized, eye is detected by PERCLOS (percentage of eye closure over time) is calculated and used to detect a fatigue condition. Keywords : Driver fatigue, machine vision, face detection, eye detecton, fatigue detection

    A VISION-BASED SYSTEM FOR MONITORING DRIVER FATIGUE

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    A VISION-BASED SYSTEM FOR MONITORING DRIVER FATIGUE Aryuanto1) F. Yudi Limpraptono2) 1,2 Department of Electrical Engineering, Institut Teknologi Nasional (ITN) Malang Jalan Raya Karanglo Km. 2 Malang 1 [email protected], 2 [email protected] Abstract This paper presents a vision-based system for monitoring driver fatigue. The system is divided into three stages: face detection, eye detection, and fatigue detection. Face detection based on the skin color segmentation is used to localize face image from a whole image. To overcome the normalized RGB chromaticity diagram is adopted. After face is localized, eye is detected by PERCLOS (percentage of eye closure over time) is calculated and used to detect a fatigue condition. Keywords : Driver fatigue, machine vision, face detection, eye detecton, fatigue detection

    Segmentation of Road Guidance Sign Symbols and Characters based on Normalized RGB Chromaticity Diagram

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    ABSTRACT In this paper, we describe a color segmentation based on the normalized RGB chromaticity diagram to extract the symbols and characters of road guidance signs. The proposed method separates blue color of the signs by utilizing the developed histogram on the normalized RGB chromaticity diagram for selecting the threshold automatically. The image morphology operator and the histogram projection technique are employed to extract the symbols and characters. From the experiments on the real scene images, the extraction rate of 97.3% is achieved

    Local Binary Patterns applied to Face Detection and Recognition

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    Nowadays, applications in the field of surveillance, banking and multimedia equipment are becoming more important, but since each application related to face analysis demands different requirements on the analysis process, almost all algorithms and approaches for face analysis are application dependent and a standardization or generalization is quite difficult. For that reason and since many key problems are still not completely solved, the face analysis research community is still trying to cope with face detection and recognition challenges. Although emulating human vision system would be the ideal solution, it is a heuristic and complicated approach which takes into account multiple clues such as textures, color, motion and even audio information. Therefore, and due to the fast evolution of technology that makes it possible, the recent trend is moving towards multimodal analysis combining multiple approaches to converge to more accurate and satisfactory results. Contributions to specific face detection and recognition applications are helpful to enable the face analysis research community to continue building more robust systems by concatenating different approaches and combining them. Therefore, the aim of this research is to contribute by exploring the Local Binary Patterns operator, motivated by the following reasons. On one hand, it can be applied to face detection and recognition and on the other hand due to its robustness to pose and illumination changes. Local Binary Patterns were first used in order to describe ordinary textures and, since a face can be seen as a composition of micro textures depending on the local situation, it is also useful for face description. The LBP descriptor consists of a global texture and a local texture representation calculated by dividing the image into blocks and computing the texture histogram for each one. The global is used for discriminating the most non-face objects (blocks), whereas the second provides specific and detailed face information which can be used not only to select faces, but also to provide face information for recognition. The results will be concatenated in a general descriptor vector, that will be later used to feed an adequate classifier or discriminative scheme to decide the face likeness of the input image or the identity of the input face in case of face recognition. It is in that stage where this research will focus, first evaluating more simple classification methods and then trying to improve face detection and recognition ratios by trying to eliminate features vector redundancy

    SEGMENTASI WARNA UNTUK EKSTRAKSI SIMBOL DAN KARAKTER PADA CITRA RAMBU LALU LINTAS

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    Penelitian ini membahas teknik segmentasi warna berbasis RGB Chromaticity Diagram ternormalisasi, untuk ekstraksi simbol dan karakter pada citra rambu lalu lintas. Teknik yang diusulkan adalah memisahkan warna biru pada latar belakang rambu petunjuk lalu lintas. Hal tersebut dilakukan dengan memanfaatkan histogram yang dikembangkan pada diagram kromatisitas untuk penentuan nilai ambang segmentasi secara otomatis. Selain itu, teknik morfologi citra dan proyeksi histogram digunakan untuk ekstraksi simbol dan karakter. Dari hasil eksperimen diperoleh bahwa teknik yang diusulkan dapat mengekstrak simbol dan karakter dengan rata-rata ekstraksi 97.3%. This research describes a normalized color segmentation technique based on RGB Chromaticity Diagram, for the extraction of symbols and characters in the image of the traffic signs. The proposed technique is to separate the blue color of the background traffic signs. This is done by using a histogram that was developed in the chromaticity diagram for the determination of the threshold value segmentation automatically. In addition, the image morphology technique and projection histogram are used for the extraction of symbols and characters. From the experimental results obtained that the proposed technique can extract symbols and characters with an average extraction is 97.3%

    Facial analysis in video : detection and recognition

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    Biometric authentication systems automatically identify or verify individuals using physiological (e.g., face, fingerprint, hand geometry, retina scan) or behavioral (e.g., speaking pattern, signature, keystroke dynamics) characteristics. Among these biometrics, facial patterns have the major advantage of being the least intrusive. Automatic face recognition systems thus have great potential in a wide spectrum of application areas. Focusing on facial analysis, this dissertation presents a face detection method and numerous feature extraction methods for face recognition. Concerning face detection, a video-based frontal face detection method has been developed using motion analysis and color information to derive field of interests, and distribution-based distance (DBD) and support vector machine (SVM) for classification. When applied to 92 still images (containing 282 faces), this method achieves 98.2% face detection rate with two false detections, a performance comparable to the state-of-the-art face detection methods; when applied to videQ streams, this method detects faces reliably and efficiently. Regarding face recognition, extensive assessments of face recognition performance in twelve color spaces have been performed, and a color feature extraction method defined by color component images across different color spaces is shown to help improve the baseline performance of the Face Recognition Grand Challenge (FRGC) problems. The experimental results show that some color configurations, such as YV in the YUV color space and YJ in the YIQ color space, help improve face recognition performance. Based on these improved results, a novel feature extraction method implementing genetic algorithms (GAs) and the Fisher linear discriminant (FLD) is designed to derive the optimal discriminating features that lead to an effective image representation for face recognition. This method noticeably improves FRGC ver1.0 Experiment 4 baseline recognition rate from 37% to 73%, and significantly elevates FRGC xxxx Experiment 4 baseline verification rate from 12% to 69%. Finally, four two-dimensional (2D) convolution filters are derived for feature extraction, and a 2D+3D face recognition system implementing both 2D and 3D imaging modalities is designed to address the FRGC problems. This method improves FRGC ver2.0 Experiment 3 baseline performance from 54% to 72%

    SEGMENTASI WARNA UNTUK EKSTRAKSI SIMBOL DAN KARAKTER PADA CITRA RAMBU LALU LINTAS

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    Abstrak Penelitian ini membahas teknik segmentasi warna berbasis RGB Chromaticity Diagram ternormalisasi, untuk ekstraksi simbol dan karakter pada citra rambu lalu lintas. Teknik yang diusulkan adalah memisahkan warna biru pada latar belakang rambu petunjuk lalu lintas. Hal tersebut dilakukan dengan memanfaatkan histogram yang dikembangkan pada diagram kromatisitas untuk penentuan nilai ambang segmentasi secara otomatis. Selain itu, teknik morfologi citra dan proyeksi histogram digunakan untuk ekstraksi simbol dan karakter. Dari hasil eksperimen diperoleh bahwa teknik yang diusulkan dapat mengekstrak simbol dan karakter dengan rata-rata ekstraksi 97.3%. Kata Kunci: citra rambu lalu lintas, ekstrasi objek, RGB chromaticity diagram, segmentasi warn
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