---
license: apache-2.0
library_name: litert
pipeline_tag: image-classification
tags:
  - litert
  - tflite
  - android
  - on-device
  - gpu
  - face-anti-spoofing
  - liveness
  - presentation-attack-detection
  - face
---

# Silent-Face Anti-Spoofing (MiniFASNetV2) — Face liveness (LiteRT GPU)

On-device **face liveness / anti-spoofing** running **fully on the LiteRT `CompiledModel`
GPU** delegate (no CPU fallback). [Silent-Face-Anti-Spoofing](https://github.com/minivision-ai/Silent-Face-Anti-Spoofing)
detects **presentation attacks** — a printed photo or a replayed screen shown to the camera
— so a live face passes and a fake is rejected. The anti-fraud building block for face
login / e-KYC. Tiny (**1.85 MB**), ~5 ms/frame on a Pixel 8a.

- **Architecture:** MiniFASNetV2 (depthwise-separable CNN) — pure CNN.
- **Weights:** [minivision-ai/Silent-Face-Anti-Spoofing](https://github.com/minivision-ai/Silent-Face-Anti-Spoofing) (`2.7_80x80_MiniFASNetV2`) · Apache-2.0.
- **Size:** 1.85 MB.

![Silent-Face liveness](hero.png)

*A photographed face is correctly flagged as a replay/presentation attack. A live camera capture scores "live". Portrait: Unsplash (free license).*

## I/O

- **Input:** `[1, 3, 80, 80]` NCHW, **BGR**, `x/255` — a face crop (~2.7× the face box, centered).
- **Output:** `[1, 3]` softmax — **class 1 = live/real**, classes 0 & 2 = spoof (print / replay).
  Live score = `output[1]`; `argmax == 1` → live.

## GPU conversion

MiniFASNetV2 is a pure CNN → fully GPU-compatible (**168/168 nodes on the delegate, 1
partition**; device corr 1.0, ~5 ms) with **zero patches** (PReLU lowers to GPU-clean
relu ops). CPU-exact vs PyTorch (corr 1.0).

## Minimal usage

### Kotlin (Android, LiteRT CompiledModel GPU)

```kotlin
val options = CompiledModel.Options(Accelerator.GPU)
val model = CompiledModel.create(context.assets, "silentface.tflite", options, null)
val inBufs = model.createInputBuffers()
val outBufs = model.createOutputBuffers()

inBufs[0].writeFloat(faceCropNCHW)       // [1,3,80,80] BGR, x/255
model.run(inBufs, outBufs)
val p = outBufs[0].readFloat()           // [3] softmax; live = p[1], spoof if argmax != 1
```

### Python (LiteRT / ai-edge-litert)

```python
import numpy as np
from ai_edge_litert.interpreter import Interpreter

it = Interpreter(model_path="silentface.tflite"); it.allocate_tensors()
inp, out = it.get_input_details(), it.get_output_details()
it.set_tensor(inp[0]["index"], x)        # [1,3,80,80] float32, BGR, x/255
it.invoke()
p = it.get_tensor(out[0]["index"])[0]    # [3]; live = p[1]
```

## Note

The original repo ensembles two MiniFASNet models (crops at scale 2.7 and 4.0) with a face
detector; this ships the primary `MiniFASNetV2` (2.7). For best accuracy, feed a detected
face crop and optionally ensemble the second model.

## License

Apache-2.0 (Silent-Face-Anti-Spoofing / minivision-ai).