Sertis Face Recognition system: An overview


Fig. 1: Deep FR System with face detector and alignment. First, a face detector is used to localize faces. Second, the faces are aligned to normalized canonical coordinates. Finally, the FR module is implemented. In the FR module, different CNN architectures and loss functions are used to extract discriminative deep features or embeddings during training; face matching methods are used to do feature classification after the embeddings of testing data are extracted. Fig. 3 from [12].


  1. FRVT 1:1 Verification
  2. FRVT 1:N Identification
  3. FRVT: Face Mask Effects
  4. FRVT Demographic Effects
  7. FRVT Twins Demonstration
  8. FRVT Paperless Travel
  9. FRVT Quality Summarization

FRVT 1:1 Verification


  • Visa images: The number of subjects in this category is of the order of 105. The images are of size 252x300 pixels, with the mean inter-ocular distance (IOD) being 69 pixels. The images are of subjects of all ages from greater than 100 countries, with a significant imbalance due to visa issuance patterns. Additionally, many of the images are live capture, and a substantial number of the images are paper photographs.
  • Border crossing images: The number of subjects in this category is of the order of 106. The images are captured with a camera oriented by an attendant toward a cooperating subject. This is done under time constraints so there are role, pitch and yaw angle variations. Also background illumination is sometimes strong, so the face is under-exposed. There is some perspective distortion due to the close range of the images. Some faces are partially cropped. Similar to the Visa images, the images are of subjects from greater than 100 countries, with significant imbalance due to population and immigration patterns. The images have a mean IOD of 38 pixels.
Fig. 2: Simulated samples of visa and border image types used for the FRVT 1:1 Verification [3].

Test Design


Table 1: Sertis FR system: FNMR @ FMR = 1.00E-06. Overall performance and international rank of the Sertis FR system on the Visa-Border cross domain verification.
Table 2: Sertis FR system: FNMR @ FMR = 1.00E-05. A showcase of the Sertis FR system’s performance being inclusive of both the demographics.

Presentation Attack Detection

  • Passive Liveness Detection : Passive liveness detection is a fraud detection method that does not require any specific actions from the user. The Sertis FR system requires only one snapshot, which is then analyzed using a deep learning model. Sertis Vision Lab has developed a SoTA passive liveness detection model, which has been trained on an in-house dataset captured in the wild. The model’s overall accuracy is 99.74% at a False Acceptance Rate (FAR) of 0.001.
  • Active Liveness Detection : Active liveness detection requires users to intentionally confirm their presence by interacting with the system as part of the process. The Sertis FR system uses live video capture to detect user interaction with the system. The current solution captures the user blinking using Sertis’ SoTA face landmarks model [2] before sending the best frame in the video sequence to the passive liveness detection model to further check for a passive video attack. The blink counting accuracy is evaluated by computing all the frames in the captured video sequence and currently stands at 99.61% and 99.21% for 30 FPS and 15 FPS videos, respectively.

Use Case



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