AIIR SDK

Face Recognition Software
Development Kit

Bring our pioneering face AI to your service.

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Easy, Light, and Compact Source
of Innovation

AIIR SDK is the complete platform for the development of face recognizing
features with the most up-to-date face detection and matching technologies.

Easy Development

Intuitively designed functions to allow quick learning & development.

Light Weight & High
Performance

Maintains high performance at low processing power and is free from the capacity limitations.

Continuous AI Engine Updates

Alchera’s Data Science Division continuously supplies machine leaning data to update performance every 6 months.

Professional Customer Success Team

Receive friendly and easy onboarding process from Alchera’s AI experts.

Top-Tier in Global Facial
Recognition Technology

Face analysis finds and analyzes faces in input video or images to identify the individuals.

3.12 Million

The number of cameras to which Alchera’s technology is applied

99.99% Under 1 second

#1 In accuracy and speed among Korean Companies in NIST FRVT

20,000 Per day

The number of images processed every day for machine leaning

Choose the Best Package to Match
Your Business Needs

You can freely configure the package you want and purchase the features you need.

Face Detection Package

Face Detection supports face detection, landmark detection, and face alignment.

$ 7.50 / device Android

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Face Recognition Package

Face Recognition supports the entire face recognition process from face detection to face matching.

$ 12.50 / device Android

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Try before implementing

Try the 30-day free version before deciding.

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Consult to see what is suitable for your business

Get in contact with our AI professionals for your needs.

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Take a Look at our Key Features of AIIR SDK

Check out the various functions and application technologies of Alchera’s AIIR SDK.

- Face detection is the first half of face recognition, and it consists of three consecutive steps, namely face detection, landmark detection, and face alignment. The process starts by locating where a face is or faces are in a visual input. Upon successfully detecting a face, the algorithm then goes on to extract landmark points that are to be used as it aligns and crops the image making it the right size so that it can be processed further.
- Face matching is the latter half of face recognition and made up of two consecutive steps including feature extraction and feature comparison. Upon receiving the aligned face image, the algorithm extracts a feature value by applying dozens of convolutional layers that gradually decrease the size of each component of a face. In the end, an nth dimension vector representing a unique face is created, and this information is then used for the verification of different people’s identity.
- Face anti-spoofing is a function that detects whether the visual input received is from a real person physically present at the point of capture, and it runs not only on IR or 3D depth cameras, but also on RGB cameras. With a true positive rate as high as 99%, which then functions as a highly effective measure for preventing identity theft, Alchera’s anti-spoofing has been applied to a variety of industries where there is a need for such physical and/or digital security.
- Age, Gender, Emotion(AGE) is a function that estimates the age, gender, and emotion evident in a person from a detected face. The most common usage of AGE is the collection and utilization of customer data for market research and/or targeted advertisement in retail businesses.
- Mask detection is a function that is used to check whether a detected face is properly wearing a facial mask. With an accuracy of 99.70%, Alchera’s mask detection technology can tell whether a person is wearing a facial mask properly or not even when the person is wearing the mask below one’s nose or chin.

Try our AIIR Face SDK Demo

Upload an image file to see how it works.

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{
    "count": 1,
    "faces": [
        {
        "box": {
            "x": 113.06485,
            "y": 84.3804,
            "width": 114.43222,
            "height": 167.66484
        },
        "landmark": {
            "lm_points": [
            {
                "x": 112.34729,
                "y": 155.71147
            },
            {
                "x": 112.34729,
                "y": 155.71147
            },
            {
                "x": 112.34729,
                "y": 155.71147
            },
            {
                "x": 112.34729,
                "y": 155.71147
            },
            {
                "x": 112.34729,
                "y": 155.71147
            },
    }
}