FINGERTEC FACE ID FACE RECOGNITION Technology Overview

The Challenges to Face Recognition The Challenges to Face Recognition Direction of Illumination Face Position While Capturing Face Expression

Direction of Illumination Direction of Illumination

Face Position and Expression Face Position and Expression

Face Recognition Process Face Recognition Process Data Collection Face Detection Facial Landmark Feature Extraction Face Alignment Dimension Reduction Face Recognition

The Architectures of Face Recognition The Architectures of Face Recognition CNN Model (Convolutional Neural Network) New face recognition technology to overcome illumination, face pose and expression.

The Architectures of Face Recognition The Architectures of Face Recognition

The Test Database The Test Database Our face algorithm are working in more 3,000,000 units of face recognition devices, which sold more than 180 countries.

Demo Main Features Demo Main Features

Face Properties Analysis Face Properties Analysis

Face Poses Analysis Face Poses Analysis

Face Recognition Face Recognition

Features FingerTec Face ID Face detection√ Face Liveness detection√ Face auto tracking√ Multi faces detecting and tracking √ Face pose Yaw±15° Pitch±10° roll±10° Face recognition 1:1√ 1:N√ Able to work when users with accessories on face and hair√ Able to work under changes of contrast√ Able to work when users’ eyes Close√ Open√ Able to work while users do mouth movements√ Able to work with users wear spectacles√ Able to differentiate multi users in one scan√ Able to determine male or female√ Able to estimate users’ ages√ Algorithm Compare Algorithm Compare

Questions and Answers Questions and Answers 1.What kind of model do you use for the face recognition? A: Our recognition software is combining heuristic and deep feature. 2.Do you need training data? If so, how much data is required? A: Yes. Require more than 10k of data. 3.Can your algorithm provide a continuous update of the enrollment? A: No, the enrolment process only required once for one driver. However driver can re-enrol anytime. 4.What features/methods do you focus on? A: We are focusing on face appearance and local feature. 5.What part of the face is used for your algorithm? A: Facial components and facial contour. 6.How do you obtain the face? A: Face come from a picture which is captured by a camera. 7.Can you deal with non-cooperative enrolment use cases? A: Our current product has only one camera to capture face, and system requires person to adjust his/her face in order to get multi-pose face. However in car driver system, there are at least two cameras, we can capture different face poses by using different camera. In case only one camera install in car, the position of camera can be adjust to cover the driver’s face/head position.

Questions and Answers Questions and Answers 8.Therefore, it would be desirable if you can answer the question how your performance is expected to decrease if the face is not positioned in a box, but must be found in the video stream. A: Similar to question above our algorithm can find the face from any place in the picture or video stream. Therefore no worries for the head position. 9.Also, what happens if we provide you with a certain region of interest where a face is found, but you need to deal with yaw, pitch, and roll angles? A: No problem as our algorithm can deal with face poses in yaw, pitch and roll angles. 10.As you are only enrolling using up and down movements, how are you able to deal with a rotated head? (I am referring to the statement that you can deal with 20 degrees variation in yaw, pitch, and roll.) A: No problem as we can deal with 30 degrees variation in yaw, pitch and roll. 11.Regarding the enrolment, it is also not feasible for our application in the car to ask the driver to position their eyes in a certain box and move their head up and down. How well are you able to deal with such a scenario? A: The video is displaying the enrolment method in our current products, which has only one camera. As discussed in Q7, 8, 9 and 10, by combining the in car camera, our recognition software does not require driver to move head to enrol.

Questions and Answers Questions and Answers 12.How much static and dynamic RAM do you use? (models, code base..) A: Currently in PC models size: 20MB, code base: 3MB.On embedded system, the library of algorithm, code size is less than 1MB, dynamic RAM size is less than 4MB. 13.How much runtime does your algorithm need? A: Minimum requirement: MIPS/ARM Processor, CPU: 400MHz, 32MB RAM, 16MHz Flash memory 14.What are the average times until you recognize a face? A: The average time to recognize a face is less than 0.5s. 15.How is your robustness to different ethnicities? A: Heuristic and deep feature from Facial components and facial contour have no problem to different ethnicities. The only challenges to face recognition are illumination, face poses and expression.

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