Person Recognition

PRW is a large-scale dataset for end-to-end pedestrian detection and person recognition in raw video frames. PRW is introduced to evaluate Person Re-identification in the Wild, using videos acquired through six synchronized cameras. It contains 932 identities and 11,816 frames in which pedestrians are annotated with their bounding box positions and identities.

The iCartoonFace dataset is a large-scale dataset that can be used for two different tasks: cartoon face detection and cartoon face recognition. Source: https://github.com/luxiangju-PersonAI/iCartoonFace Image Source: https://github.com/luxiangju-PersonAI/iCartoonFace

CN-Celeb-AV is a multi-genre AVPR dataset collected 'in the wild'. This dataset contains more than 420k video segments from 1,136 persons from public media. Source: CN-Celeb-AV: A Multi-Genre Audio-Visual Dataset for Person Recognition Image Source: CN-Celeb-AV: A Multi-Genre Audio-Visual Dataset for Person Recognition

Dataset (part 1/3) for Motion Patterns Identification part of MotionID: Human Authentication Approach. Data type: bin (should be converted by attached notebook). Six users each with a Samsung Galaxy S10e smartphone collected IMU data every day for 2 weeks. At the end of two weeks, the users switched smartphones with each other and restarted the process. Each user spent 2 weeks per smartphone during the whole data collection process, which took 12 weeks in total. Throughout the experiment, the Galaxy S10e was the main and only device of each user. The smartphones were used habitually and ordinarily, with the only difference from real-life scenarios being that the data collection app was always on. Each measurement has a corresponding timestamp. Screen.txt file consists of timestamps with current status of device: 1) SCREEN_OFF - a phone turned off 2) SCREEN_ON - a phone is on 3) USER_PRESENT - a phone has just been unlocked

Dataset (part 2/3) for Motion Patterns Identification part of MotionID: Human Authentication Approach. Data type: bin (should be converted by attached notebook). Six users each with a Samsung Galaxy S10e smartphone collected IMU data every day for 2 weeks. At the end of two weeks, the users switched smartphones with each other and restarted the process. Each user spent 2 weeks per smartphone during the whole data collection process, which took 12 weeks in total. Throughout the experiment, the Galaxy S10e was the main and only device of each user. The smartphones were used habitually and ordinarily, with the only difference from real-life scenarios being that the data collection app was always on. Each measurement has a corresponding timestamp. Screen.txt file consists of timestamps with current status of device: 1) SCREEN_OFF - a phone turned off 2) SCREEN_ON - a phone is on 3) USER_PRESENT - a phone has just been unlocked

Dataset (part 3/3) for Motion Patterns Identification part of MotionID: Human Authentication Approach. Data type: bin (should be converted by attached notebook). Six users each with a Samsung Galaxy S10e smartphone collected IMU data every day for 2 weeks. At the end of two weeks, the users switched smartphones with each other and restarted the process. Each user spent 2 weeks per smartphone during the whole data collection process, which took 12 weeks in total. Throughout the experiment, the Galaxy S10e was the main and only device of each user. The smartphones were used habitually and ordinarily, with the only difference from real-life scenarios being that the data collection app was always on. Each measurement has a corresponding timestamp. Screen.txt file consists of timestamps with current status of device: 1) SCREEN_OFF - a phone turned off 2) SCREEN_ON - a phone is on 3) USER_PRESENT - a phone has just been unlocked

Dataset for User Verification part of MotionID: Human Authentication Approach. Data type: bin (should be converted by attached notebook). ~50 hours of IMU (Inertial Measurement Units) data for one specific motion pattern, provided by 101 users. For data collection only one smartphone (Samsung Galaxy S20) was used. The data was collected by 101 users, each of whom lifted the smartphone from the table 300 times, 50 times for each of the 6 locations of the device (see picture below). The data collection procedure for user verification was as follows: 1) The user lifts the locked smartphone from the table surface to a comfortable level 2) The user unlocks the smartphone via an in-display ultrasonic fingerprint sensor 3) The user locks the smartphone via Home button and puts the device 4) The cycle is repeated for 50 times at each location, for a total of 300 times per person Each measurement has a corresponding timestamp. Screen.txt file consists of timestamps with current status of device: 1) SCREEN_OFF - a phone turned off 2) SCREEN_ON - a phone is on 3) USER_PRESENT - a phone has just been unlocked