# Facial Landmark Detection Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/facial-landmark-detection
> Markdown URL: https://aitinkerers.org/technologies/facial-landmark-detection.md
> Technology record last updated: 2026-05-25T08:22:12Z
> Generated: 2026-08-26T05:24:07Z

Facial Landmark Detection is a core computer vision task that uses deep learning models (e.g., MediaPipe) to accurately pinpoint and track key fiducial points on a face, such as the 478-point mesh, in real-time.

This technology is the foundation for advanced face analysis, leveraging Convolutional Neural Networks (CNNs) and other deep learning methods to map facial geometry. It functions by identifying a set of key coordinates (x, y, z) on features like the eyes, nose, mouth, and jawline, often using models like Dlib’s 68-point predictor or the more detailed 478-point Face Landmarker. The output provides a robust, pose-invariant representation of the face, which is critical for downstream applications. Specifically, F.L.D. drives high-value use cases: real-time Augmented Reality (AR) filters, precise facial expression analysis (blendshapes), 3D face reconstruction, and high-accuracy face alignment for recognition systems.

- Official technology site: https://developers.google.com/mediapipe/solutions/vision/face_landmarker
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Speak mk1: A multimodal mamba-attention hybrid model for speech therapy](https://dubai.aitinkerers.org/talks/rsvp_f9s0FLsNTT0)

I am currently building a mamba-attention hybrid encoder and decoder from scratch for speech therapy, not fine-tuning, not wrapping an API, but training every component end to end on my RTX 4060 laptop. The system has three components I am building in parallel: The first is a custom Mamba SSM-based audio encoder trained on LibriSpeech with multi-task phonological heads for voicing, manner, place of articulation, and correctness detection. The encoder uses a BLIP-2-style Q-Former to bridge audio representations into the language model. The second is a video pipeline using MediaPipe FaceLandmarker to isolate and analyze oral region motion frame by frame, extracting articulatory features like tongue tip position, mouth opening geometry, and lip protrusion in real time from a standard webcam. The third component is SpeakMK1LLM, which serves as the project’s core reasoning engine. To balance cutting-edge architectural research with deployment stability for the current phase, we have developed two iterations of this model: The Hybrid Prototype: A custom 70M parameter Mamba-attention hybrid model designed for high-efficiency sequence modeling. It was trained on a rigorous four-stage curriculum: general pretraining on Tinystories, domain adaptation on CHILDES, clinical knowledge injection from pubmed central, and final instruction tuning. The Deployment Model: A fine-tuned Gemma 3n E4B, which leverages the same four-stage curriculum to adapt its large-scale reasoning capabilities to the specific nuances of Speech-Language Pathology (SLP). The entire stack requires only a webcam and a microphone, which is the point — making clinical-quality articulation feedback accessible without specialized hardware or a therapist present in the room. For the demo I would show the live pipeline taking a child's speech, flagging a specific phoneme error, and generating a graded corrective prompt in real time, alongside the clinician dashboard that logs session data for remote review.

- Event context: AI Tinkerers Dubai - May Demo Day — 2026-05-23 — Dubai
- Public talk page: https://dubai.aitinkerers.org/talks/rsvp_f9s0FLsNTT0

## Related Technologies

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- [Gemma 3n E4B](https://aitinkerers.org/technologies/gemma-3n-e4b) ([Markdown](https://aitinkerers.org/technologies/gemma-3n-e4b.md)) — 1 public demo
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