SNResNet: A New Architecture Based on SqNxt Blocks and Rish Activation for Efficient Face Recognition
Bibliographic record
Abstract
In this paper, we present a novel face recognition architecture based on the Inception-ResNet framework, called SNResNet.The Inception-ResNet architecture, is effective in computer vision applications but exhibits limitations such as computational complexity, high memory consumption, and data dependency.It uses the ReLU activation function and softmax loss function which are not best-suited for face recognition.The proposed SNResNet uses triplet loss as the loss function to be able to train the model on large datasets.The advantages of the triplet loss over the softmax are handling one-shot learning, robustness to class imbalance and fine-grained discrimination.The ReLU activation function rejects all negative values that in some applications reduce the accuracy of the model.To overcome this problem, we introduced a new activation function called Rish which has better performance.In addition, we optimized the Inception-ResNet-B block using the SqNxt block to control the model's computational costs.The CASIA-WebFace dataset is used to train the models.This dataset has some challenges; e.g., some photos have more than one face, and all faces have a background.Preprocessing conditions are defined to identify and align the correct face.SNResNet achieves 94.63% accuracy on CASIA-WebFace.Performance evaluation on the LFW benchmark database yields an impressive accuracy of 99.68%, surpassing the standard model's accuracy of 98.85%.Further, we reduced the FLOPS of the Inception-ResNet model by 15.61% which indicates a lower computational cost and a faster model for face recognition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".