Face detection

Mon Jan 24 2022

Face detection is one of the main elements of all human-centered applications of artificial intelligence. Face is one of the important biometrics in authentication systems. Face detection in real-world images is challenging due to different shapes, orientations, sizes, colors and occlusion due to wearing glasses, hat, and etc. In the last decade, a few promising algorithms have been proposed in this AI field of research. Here, at saiwa face detection service, we provide two methods: 1. Dlib open source cross platform library, and 2. Multitask cascaded convolutional network (MTCNN) method. The first method is known for its performance and low computational time complexity while the second method is more accurate and employs recent advances in the field of deep learning. For more technical details of these methods, please refer to the corresponding white paper. In addition to detecting faces, saiwa face detection service also detects facial features like eyes, eyebrows, nose, mouth and chin. Below you may find a few advantages of using this service:

Providing state-of-the-art algorithms
Detecting and localizing human face regions of interests (ROIs)
Detecting occluded faces in real-world applications
High performance and high accuracy
Detecting facial features (face landmarks)
Exporting and archiving the results in user cloud space or locally
Apply on several images
Preview and download the resulting images or detailed information of detected facesFace detection applications include but are not limited to:1. Face recognition for authentication a. Entrance gates b. Unlock phones, tablets, and etc. c. Identify people in social networks d. Recognize fans in sport events e. Track student attendance in classes 2. Facial expressions recognition 3. Marketing a. Advertisement recommendation according to age and gender b. Customer statistics for shelf planning 4. Eye tracking 5. Photography 6. Lip reading 7. Blind people assistant