Facial expressions are highly informative for computers to understand and interpret a person's mental and physical activities. However, continuously tracking facial expressions, especially when the user is in motion, is challenging. This paper presents NeckFace, a wearable sensing technology that can continuously track the full facial expressions using a neck-piece embedded with infrared (IR) cameras. A customized deep learning pipeline called NeckNet based on Resnet34 is developed to learn the captured infrared (IR) images of the chin and face and output 52 parameters representing the facial expressions. We demonstrated NeckFace on two common neck-mounted form factors: a necklace and a neckband (e.g., neck-mounted headphones), which was evaluated in a user study with 13 participants. The study results showed that NeckFace worked well when the participants were sitting, walking, or after remounting the device. We discuss the challenges and opportunities of using NeckFace in real-world applications.

This paper presents SpeeChin, a smart necklace that can recognize 54 English and 44 Chinese silent speech commands. A customized infrared (IR) imaging system is mounted on a necklace to capture images of the neck and face from under the chin. These images are first pre-processed and then deep learned by an end-to-end deep convolutional-recurrent-neural-network (CRNN) model to infer different silent speech commands. A user study with 20 participants (10 participants for each language) showed that SpeeChin could recognize 54 English and 44 Chinese silent speech commands with average cross-session accuracies of 90.5% and 91.6%, respectively. To further investigate the potential of SpeeChin in recognizing other silent speech commands, we conducted another study with 10 participants distinguishing between 72 one-syllable nonwords. Based on the results from the user studies, we further discuss the challenges and opportunities of deploying SpeeChin in real-world applications.

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C-Face (Contour-Face) is an ear-mounted wearable sensing technology that uses two miniature cameras to continuously reconstruct facial expressions by deep learning contours of the face. When facial muscles move, the contours of the face change from the point of view of the ear-mounted cameras. These subtle changes are fed into a deep learning model which continuously outputs 42 facial feature points representing the shapes and positions of the mouth, eyes and eyebrows. To evaluate C-Face, we embedded our technology into headphones and earphones. We conducted a user study with nine participants. In this study, we compared the output of our system to the feature points outputted by a state of the art computer vision library (Dlib 1) from a font facing camera. We found that the mean error of all 42 feature points was 0.77 mm for earphones and 0.74 mm for headphones. The mean error for 20 major feature points capturing the most active areas of the face was 1.43 mm for earphones and 1.39 mm for headphones. The ability to continuously reconstruct facial expressions introduces new opportunities in a variety of applications. As a demonstration, we implemented and evaluated C-Face for two applications: facial expression detection (outputting emojis) and silent speech recognition. We further discuss the opportunities and challenges of deploying C-Face in real-world applications.