Extremely dense face registration: Comparing automatic landmarking algorithms for general and ethno-gender models

Registration is a very important step in object recognition. Accurate detection of the eye centers, eye corners, mouth and nose are critical for face recognition and more broadly, for face processing. In this work, we have evaluated three techniques, namely AAM, Stacked ASM and CLM, for automatic detection of landmarks under the problem of extremely dense registration scheme for the face. Further we compare the efficacy of these techniques for the general case and for the specific case based on ethnicity and gender. It is shown that the performance of STASM and CLM are comparable and better than AAM. It is also shown that, in general, models trained on ethno-gender groups perform better than the models trained on general exemplars.