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Hi, Actually, measuring illumination amplitude may be not correlated to your application context. Indeed, your interest is related to facial features, luminance is only a distractor. So your features extraction should rather be robust against luminance rather than finding thresholds, etc to adapt to it in a risky way. In previous research, we used as a preprocessing tool the retina model recently proposed in OpenCV (check the contrib module). The aim was to limit the impact of lighting and enhance local face features. It can also enable face motion extraction. Have a look at it, it can improve your algorithm robustness and generalization potential.

Regards Some references : Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773: http://dx.doi.org/10.1016/j.cviu.2010.01.011. Benoit A., Caplier A., "FUSING BIO-INSPIRED VISION DATA FOR SIMPLIFIED HIGH LEVEL SCENE INTERPRETATION: APPLICATION TO FACE MOTION ANALYSIS", Elsevier,Computer Vision and Image Understanding 114 (2010), pp. 774-789: http://dx.doi.org/10.1016/j.cviu.2010.01.010.

Hi, Actually, measuring illumination amplitude may be not correlated to your application context. Indeed, your interest is related to facial features, luminance is only a distractor. So your features extraction should rather be robust against luminance rather than finding thresholds, etc to adapt to it in a risky way. In previous research, we used as a preprocessing tool the retina model recently proposed in OpenCV (check the contrib module). The aim was to limit the impact of lighting and enhance local face features. It can also enable face motion extraction. Have a look at it, it can improve your algorithm robustness and generalization potential.

Regards Some references : Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773: http://dx.doi.org/10.1016/j.cviu.2010.01.011. http://dx.doi.org/10.1016/j.cviu.2010.01.011. Benoit A., Caplier A., "FUSING BIO-INSPIRED VISION DATA FOR SIMPLIFIED HIGH LEVEL SCENE INTERPRETATION: APPLICATION TO FACE MOTION ANALYSIS", Elsevier,Computer Vision and Image Understanding 114 (2010), pp. 774-789: http://dx.doi.org/10.1016/j.cviu.2010.01.010.http://dx.doi.org/10.1016/j.cviu.2010.01.010.