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Facial features fitting using active / Yew Chuu Tian

Facial features fitting using active_Yew Chuu Tian_E3_2010_875003555_00003084245_NI

Abstract Projek ini adalah tentang pembangunan sistem untuk mengesan keadaan ciri-ciri wajah berdasarkan Model Penampilan Aktif (AAM) yang cukup cepat untuk dilaksanakan dalam masa benar bagi mengesan kewaspadaan pemandu. Untuk mengesan wajah pemandu dalam keadaan gelap ataupun pencahayaan redup, kita boleh menggunakan kamera inframerah, yang menangkap urutan gambar dalam video skala kelabu. Bagaimanapun, disebabkan oleh AAM dapat juga diterapkan dalam skala kelabu dan juga gambar berwarna dengan menggunakan kamera biasa, maka kamera jenis ini digunakan di dalam projek ini. Untuk menggunakan AAM, kita perlu membina AAM dengan menanda sejumlah gambar, kemudian dilatih dengan menerapkan Analisis Komponen Utama kepada bentuk, tekstur, dan model gabungan untuk mengurangkan jumlah parameter. Setelah model siap dibina, maka model akan dipadankan ke wajah imej masukan. Untuk memadankannya kepada wajah imej masukan, algoritma pengesanan wajah AdaBoost digunakan untuk mengesan kawasan wajah. Ini kemudiannya disusuli dengan menerapkan iteratif pembaikan AAM kepada model tersebut. Setelah model dipadankan dengan wajah, keadaan ciriciri wajah dikesan dengan mengira perbezaan di antara koordinat ciri-ciri yang telah dipadankandengan data model bentuk. _____________________________________________________________________________________ This project is about the development of a system to detect the state of facial features based on Active Appearance Model (AAM) that is fast enough to be implemented in real-time processing to detect the driver’s vigilance. To detect the driver’s face in dark or dim lighting condition, we can use infrared camera, which takes image sequence in grayscale video. However, since the current AAM can be applied in grayscale and even color images using normal cameras, this project concentrates only on this type of camera. To use AAM, we need to construct the AAM by hand-marking a number of images, then train it by applying Principal Component Analysis to the shape, texture, and the combined model to reduce the number of parameters. After the model has been built, it is fitted to the face of the input image. To fit it onto the face of input image, AdaBoost face detection algorithm is implemented to detect the face region. Subsequently, AAM iterative model refinement is applied to the model. After fitting the model to the face, the facial features state is recognized by calculating the difference between the fitted features coordinate and the shape model data. xi

Contributor(s): Yew, Chuu Tian - Author Primary Item Type: Final Year Project Identifiers: Accession Number : 875003555 Barcode : 00003084245 Language: English Subject Keywords: development of a system to detect the state of facial features based on Active Appearance Model (AAM) that is fast enough to be implemented in real-time processing to detect the driver’s vigilance; detect the driver’s face in dark or dim lighting condition; infrared camera, which takes image sequence in grayscale video First presented to the public: 1/4/2010 Original Publication Date: 3/14/2018 Previously Published By: Universiti Sains Malaysia Place Of Publication: School of Electrical & Electronic Engineering Citation: Extents: Number of Pages - 77 License Grantor / Date Granted:   / ( View License ) Date Deposited 2018-03-14 15:51:42.407 Date Last Updated 2019-01-07 11:24:32.9118 Submitter: Nor Hayati Ismail

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