Embedded implementation of facial landmarks detection using extended active shape model approach

Abstract

Facial landmark detection is very crucial for different kinds of video analytics in robotic applications. However, resource limited characteristics of an embedded system pose many challenges to implement a standalone robotic vision systems like human machine interaction, driver drowsiness monitoring and surveillance applications. The main objective of this paper is to propose effective implementation strategies for the extended Active Shape Model (ASM) based facial landmark detection algorithm using programmable resource constraint digital video processor and evaluate it in real-time. In extended ASM approach, robust detection of face, eyes and mouth is very important to initialize the shape model which defined by facial landmarks. Using this initialized shape model, the landmarks of the given test face are obtained through minimizing the distance with learned shape model iteratively. For different face parts detection, we have used robust and fast Viola-Jones detector (V-J detector) and optimized it at various levels to meet the computation and memory constraints. The implemented embedded vision system has been evaluated using visible light camera in real-world scenarios and its robustness and time consumption has been reported in detail. Through different experiments, we show that the proposed implementation framework achieves more than 98% face parts detection rate and mean error of less than 2 pixels in facial landmarks accuracy.

Driver Distraction Using Visual-Based Sensors and Algorithms

“…Authors optimized the library for the Texas Instruments C64x/C64x+ DSP cores. Karuppusamy et al [226] proposed an embedded implementation of facial landmarks detection based on both Viola-Jones face detector and facial landmarks detection using extended Active Shape Model (ASM) [227]. However, DSPs imply a much higher cost compared with other options such as field-programmable gate arrays (FPGAs) [228].…”