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en:tc_whitepaper_introducing_facestation2 [2023/08/01 16:53] mwkim [Ergonomic design] |
en:tc_whitepaper_introducing_facestation2 [2023/08/01 17:04] (current) mwkim |
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| - | Biometric authentication systems use physical traits such as fingerprint, face, iris and vein as a credential. Among others, face recognition terminals use face as a credential, which is the most similar way for a human to recognize a person. Due to this nature of the face recognition technology, people feel comfortable to perform the face authentication. | + | Biometric authentication systems use physical traits such as fingerprint, face, iris and vein as a credential. Among others, face authentication terminals use face as a credential, which is the most similar way for a human to recognize a person. Due to this nature of the face authentication technology, people feel comfortable to perform the face authentication. |
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| - | Fingerprint recognition devices, which are the most commonly used for biometric authentication, are equipped with an optical sensor. As the face recognition utilizes cameras to identify a person, it does not require an optical sensor. Thus, it allows a user to perform authentication without physical contact. Since face recognition terminals use IR (Infrared) technology, one of the major drawbacks of face recognition is the limitation of installable location: a face recognition terminal will perform poorly when it’s installed outside or near windows because of the strong ambient light. | + | Fingerprint recognition devices, which are the most commonly used for biometric authentication, are equipped with an optical sensor. As the face authentication utilizes cameras to identify a person, it does not require an optical sensor. Thus, it allows a user to perform authentication without physical contact. Since face authentication terminals use IR (Infrared) technology, one of the major drawbacks of face authentication is the limitation of installable location: a face authentication terminal will perform poorly when it’s installed outside or near windows because of the strong ambient light. |
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| - | FaceStation 2, Suprema’s newest face recognition terminal, goes beyond this limitation. It is an access control and time and attendance terminal, featuring better user experience with Android 5.0 Lollipop and Suprema’s latest algorithm, hardware and software. A wide range of functions and enhanced performance will provide users with a whole new different non-contact biometric authentication experience. | + | FaceStation 2, Suprema’s newest face authentication terminal, goes beyond this limitation. It is an access control and time and attendance terminal, featuring better user experience with Android 5.0 Lollipop and Suprema’s latest algorithm, hardware and software. A wide range of functions and enhanced performance will provide users with a whole new different non-contact biometric authentication experience. |
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| - | ===== Enhanced face recognition technology and near infrared LED ===== | + | ===== Enhanced face authentication technology and near infrared LED ===== |
| - | Processing ambient light is critical to the face recognition performance. Most of face recognition terminals out in the market have limited installable locations because the recognition performance varies depending on the strength of ambient light. | + | Processing ambient light is critical to the face authentication performance. Most of face authentication terminals out in the market have limited installable locations because the recognition performance varies depending on the strength of ambient light. |
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| Another technology adopted by FaceStation 2 to enhance the performance is pixel intensity distribution analysis. One of the difficulties in using near infrared LED technology is the effects of ambient lighting. Ambient lighting can make near infrared LED lighting useless since it contains ultraviolet rays. Plus, the shadow on a face cast by ambient light can make it difficult for the face feature extraction algorithm to extract facial features. \\ | Another technology adopted by FaceStation 2 to enhance the performance is pixel intensity distribution analysis. One of the difficulties in using near infrared LED technology is the effects of ambient lighting. Ambient lighting can make near infrared LED lighting useless since it contains ultraviolet rays. Plus, the shadow on a face cast by ambient light can make it difficult for the face feature extraction algorithm to extract facial features. \\ | ||
| - | The three-dimensional pixel intensity distribution analysis minimizes the effects of ambient light when acquiring face images. As a result, the terminal acquires near infrared images with the minimal variation of contrast. It is easier for the algorithm to recognize the shape of the face with these uniform contrast images than with too bright or dark images, so it can extract more varied features, thus can create high quality face templates. High quality face templates are crucial for the performance of face recognition. | + | The three-dimensional pixel intensity distribution analysis minimizes the effects of ambient light when acquiring face images. As a result, the terminal acquires near infrared images with the minimal variation of contrast. It is easier for the algorithm to recognize the shape of the face with these uniform contrast images than with too bright or dark images, so it can extract more varied features, thus can create high quality face templates. High quality face templates are crucial for the performance of face authentication. |
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