Human Face Tracking and Modeling using Active Appearance Model with Motion Estimation

• 발행기관: 한국스마트미디어학회
• 저자명: Hong Tai Tran In Seop Na Young Chul Kim Soo Hyung Kim
• 간행물 정보: 『스마트미디어저널』Vol6, No.3, 49~56쪽, 전체 8쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2017.09.30

영문 초록

Images and Videos that include the human face contain a lot of information. Therefore, accurately extracting human face is a very important issue in the field of computer vision. However, in real life, human faces have various shapes and textures. To adapt to these variations, A model-based approach is one of the best ways in which unknown data can be represented by the model in which it is built. However, the model-based approach has its weaknesses when the motion between two frames is big, it can be either a sudden change of pose or moving with fast speed. In this paper, we propose an enhanced human face-tracking model. This approach included human face detection and motion estimation using Cascaded Convolutional Neural Networks, and continuous human face tracking and modeling correction steps using the Active Appearance Model. A proposed system detects human face in the first input frame and initializes the models. On later frames, Cascaded CNN face detection is used to estimate the target motion such as location or pose before applying the old model and fit new target.

목차

I. INTRODUCTION

II. BACKGROUND

III. PROPOSED SYSTEM

IV. EXPERIMENTAL RESULTS

V. CONCLUSION

키워드

Image Processing Human face tracking Active Appearance Model