The harmful effects of cell phone usage on driver behavior have been well investigated and the growing problem has motivated several several research efforts aimed at developing automated cell phone usage detection systems. Computer vision based approaches for dealing with this problem have only emerged in recent years. In this paper, we present a vision based method to automatically determine if a driver is holding a cell phone close to one of his/her ears (thus keeping only one hand on the steering wheel) and quantitatively demonstrate the method's efficacy on challenging Strategic Highway Research Program (SHRP2) face view videos from the head pose validation data that was acquired to monitor driver head pose variation under naturalistic driving conditions. To the best of our knowledge, this is the first such evaluation carried out using this relatively new data. Our approach utilizes the Supervised Descent Method (SDM) based facial landmark tracking algorithm to track the locations of facial landmarks in order to extract a crop of the region of interest. Following this, features are extracted from the crop and are classified using previously trained classifiers in order to determine if a driver is holding a cell phone. We adopt a through approach and benchmark the performance obtained using raw pixels and Histogram of Oriented Gradients (HOG) features in combination with various classifiers.

No abstract

In this paper we propose a novel Contourlet Appearance Model (CAM) that is more accurate and faster at localizing facial landmarks than Active Appearance Models (AAMs). Our CAM also has the ability to not only extract holis tic texture information, as AAMs do, but can also extract local texture information using the Nonsubsampled Con tourlet Transform (NSCT). We demonstrate the efficiency of our method by applying it to the problem of facial age es timation. Compared to previously published age estimation techniques, our approach yields more accurate results when tested on various face aging databases.

Abstract. In this paper we address the problem of automatically locating the facial landmarks of a single person across frames of a video sequence. We propose two methods that utilize Kalman filter based approaches to assist an Active Shape Model (ASM) in achieving this goal. The use of Kalman filtering not only aids in better initialization of the ASM by predicting landmark locations in the next frame but also helps in refining its search results and hence in producing improved fitting accuracy. We evaluate our tracking methods on frames from three video sequences and quantitatively demonstrate their reliability and accuracy.

No abstract