Facial Landmark Detection
Facial Landmark Detection - Related Videos
04:47
Systematic Bronchoscopy: the Four Landmarks Approach
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Cited by 6 •
2023
Here, we present a protocol to navigate the bronchial maze in a structured manner, splitting the bronchoscopy into a stepwise approach-the four landmarks approach.
03:37
Performing Crush Axotomy on the Facial Nerve in a Mouse Model
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2025
Source: Olmstead, D. N., et al., Facial Nerve Axotomy in Mice: A Model to Study Motoneuron Response to Injury. J. Vis. Exp. (2015).This video demonstrates the facial nerve crush procedure in a mouse, where blunt dissection through key landmarks exposes the facial nerve trunk at the stylomastoid foramen. Forceps are then used to compress the nerve trunk, inducing axonal damage and disrupting neuronal signal transmission.
06:26
Quantitative Assessment Protocol for Facial Soft Tissue Volumetric Changes with Stereophotogrammetry
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2025
We present a detailed protocol for three-dimensional analysis of soft tissue changes with the use of stereophotogrammetry.
10:11
Facial Nerve Axotomy in Mice: A Model to Study Motoneuron Response to Injury
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Cited by 28 •
2015
We present a surgical protocol detailing how to perform a cut or crush axotomy on the facial nerve in the mouse. The facial nerve axotomy can be employed to study the physiological response to nerve injury and test therapeutic techniques.
05:22
Systematic Endobronchial Ultrasound - The Six Landmarks Approach
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Cited by 2 •
2023
Endobronchial ultrasound-guided sampling using transbronchial needle aspiration plays a key role in staging and diagnosing lung cancer. We propose a systematic stepwise approach dividing the procedure into six landmarks that should be taught to new operators.
FAQs
A trained model examines visual features in an image or video frame and predicts numerical coordinates for facial points. Regression methods estimate these coordinates directly, while neural-network methods learn visual patterns associated with landmark locations. The resulting coordinates convert facial appearance into measurable geometry, allowing engineering systems to analyze position, alignment, and movement rather than relying only on raw pixel information.
Tracking the same facial points across successive frames makes it possible to measure movement and alignment over time. Changes in the coordinates can represent shifts in the eyes, mouth, jawline, or other tracked regions. This temporal information supports expression analysis and interactive systems, because the engineering application can respond to changing facial geometry instead of analyzing each frame as an isolated image.
Pose, lighting, occlusion, and image resolution can all change how reliably a model identifies facial points. Extreme pose may alter the visible geometry, poor lighting can obscure visual features, occlusion can hide landmarks, and low resolution provides less spatial information. Engineers must therefore interpret coordinate estimates in relation to the image conditions under which they were produced.
Landmark detection produces a numerical geometric representation based on selected facial points, whereas raw image appearance contains the complete visual information of the frame. This coordinate-based representation makes alignment, movement, and relative facial geometry easier to measure. It can also serve as a focused input for engineering tasks such as expression analysis or augmented-reality effects.
A typical workflow supplies an image or video frame to a trained model, obtains predicted coordinates for consistent facial points, and then uses those coordinates for measurement or alignment. For video, the points can be tracked across frames to evaluate movement. The final processing stage depends on the application, such as expression analysis, interaction, or visual effects.
Engineers can use landmark coordinates to determine facial geometry and alignment before positioning an augmented-reality effect. Points around the eyes, nose, mouth, or jawline provide references for how the effect should relate to the face. Tracking those references across video frames helps the effect respond to facial movement, while pose, occlusion, lighting, and resolution influence the result.
The numerical coordinates provide a measurable interface between visual input and engineered behavior. Systems can use changes in facial geometry to study movement, analyze expressions, align images, or support human-computer interaction. In image-based research, the representation also enables comparisons of facial position and alignment across frames, although conclusions remain dependent on detection accuracy under the recorded conditions.