Changelog Livennes

2.5

• General optimization of the capture and validation flow to improve stability across consecutive sessions.
• Performance adjustments to reduce latency when returning results to the host.
• Initial improvements in evidence reduction (Base64) to decrease payload size.

2.6.0

• Camera pipeline optimization for smoother performance on mid-range devices.
• Evidence compression adjustments to reduce transfer size and improve delivery times.
• UX improvements with clearer feedback during alignment and capture.

2.7.0

• Facial tracking optimization for better face stability within the frame.
• Performance improvements in frame processing to reduce sustained CPU/GPU load.
• Flow logic adjustments to improve consistency between capture, analysis, and results.

2.8.0

• Optimization of evidence return to the host through more efficient Base64 generation and encoding.
• Further reduction of image/evidence size while maintaining reference quality.
• Visual experience improvements for greater consistency across resolutions and devices.

2.9.0

• Optimization of camera startup and shutdown, as well as flow recovery during retries.
• Memory usage improvements to prevent performance degradation after multiple runs.
• User feedback adjustments to reduce friction and increase process clarity.

3.0.0

• Major SDK evolution focused on performance and lifecycle stability of the component.
• Analysis pipeline optimization for smoother execution and more consistent response times.
• Improvements in resource handling (camera and memory) for longer sessions.

3.0.3

• Optimization of camera session startup and transition into analysis to reduce startup times.
• Efficiency adjustments to reduce CPU/GPU spikes during initialization.

3.0.8

• Improvements in evidence compression and serialization: lighter Base64 and faster delivery to the host.
• Flow optimization to reduce latency between capture and final result.

3.1.0

• Logic adjustments for greater consistency in the validation flow.
• Memory usage optimization in repeated sessions to improve stability.

3.1.5

• UX improvements with clearer and more consistent feedback during the capture process.
• Performance optimization on resource-constrained devices to maintain smoothness.

3.2.0

• Optimization of the retry system without full reinitialization to improve flow continuity.
• Evidence compression adjustments to reduce payload size while maintaining adequate quality.

3.2.6

• Optimization of frame processing to reduce sustained CPU/GPU load.
• Camera pipeline stability improvements for uninterrupted analysis.

3.3.0

• Increased robustness of facial analysis under lighting variations and moderate movement.
• Optimization of evidence generation and encoding for faster return to the host.
• Performance adjustments to maintain more stable FPS during the session.

3.3.7

• Optimization of camera module initialization time and analysis preparation.
• Internal adjustments for a more stable experience during prolonged runs.

3.4.0

• Facial tracking optimization for greater consistency of the face throughout the session.
• Performance improvements in evidence compression (Base64) to reduce transfer size and timing.
• Visual experience adjustments for consistency across different resolutions and devices.

3.4.6

• Optimization of memory usage and resource release at the end of the flow to maintain stability.
• UI/Render performance improvements through reduced style load and smoother transitions.

3.5.0.1

• Integration of the new Liveness engine with advanced face detection and emotion/gesture analysis.
• Optimization of the capture and validation flow.
• Design and user experience improvements (UI/UX).
• Reduced latency in the camera pipeline.
• Greater robustness against flow manipulation.

3.5.2.0

• Improved facial detection system for gesture recognition.
• Specific optimization for devices with Snapdragon 8 Elite.
• Strengthened anti-spoofing signals.
• Greater efficiency in resource consumption during active sessions.

3.5.2.1

• Optimization of component initialization and instantiation.
• Localization and language consistency improvements.
• Additional performance adjustments.
• Refinement of control logic and result delivery.