{'fraction_non_alphanumeric': 0.01288659793814433, 'fraction_numerical': 0.005154639175257732, 'mean_word_length': 4.798507462686567, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Improving mobile device interaction by eye tracking analysis

This paper describes a non-intrusive eyetracking tool for mobile devices by using images acquired by the front camera of the iPhone and iPod Touch. By tracking and interpreting the user's gaze to the smartphone's screen coordinates the user can interact with the device by using a more natural and spotaneous way. The application uses a Haar classifier based detection module for identifying the eyes in the acquired images and subsequently the CAMSHIFT algorithm to find and track the eyes movement and detect the user's gaze. The performance of the proposed tool was evaluated by testing the system on 16 users and the results shown that in about 79% of the times it was able to detect correctly the users' gaze.