AI Summary & Key Takeaways
AI InsightFrom the experimental results, we showed that the proposed AIA model provides higher accuracy than classical Active Appearance Model for face alignment in a point-to-point error sense.
📌 3 Key Takeaways
Abstract
Illumination conditions have an explicit effect on the performance of face recognition systems. In particular, varying the illumination upon the face imposes such complex effects that the identification often fails to provide a stable performance level. In this paper, we propose an approach integrating face identity and illumination models in order to reach acceptable and stable face recognition rates. For this purpose, Active Appearance Model (AAM) and illumination model of faces are combined in order to obtain an illumination invariant face localization. The proposed method is an integrated Active Illumination and Appearance Model (AIA) which combines identity, illumination and shape components in a single model and allows us to control them, separately. One of the major advantage of the proposed AIA model is that efficient model fitting is achieved, whilst maintaining performance against illumination changes. In addition to model fitting, images illuminated from different directions can easily be synthesized by changing the parameters related to illumination modes. The method provides a practical approach, since only one image with frontal illumination of each person for training, is sufficient. There is no need to build complex models for illumination. As a result, this paper has presented a simple and efficient method for face modeling and face alignment in order to increase the performance of face localization by means of the proposed illumination invariant AIA method for face alignment, such as the Active Appearance Models, invariant to changes in illumination. From the experimental results, we showed that the proposed AIA model provides higher accuracy than classical Active Appearance Model for face alignment in a point-to-point error sense.
Özet
Illumination conditions have an explicit effect on the performance of face recognition systems. In particular, varying the illumination upon the face imposes such complex effects that the identification often fails to provide a stable performance level. In this paper, we propose an approach integrating face identity and illumination models in order to reach acceptable and stable face recognition rates. For this purpose, Active Appearance Model (AAM) and illumination model of faces are combined in order to obtain an illumination invariant face localization. The proposed method is an integrated Active Illumination and Appearance Model (AIA) which combines identity, illumination and shape components in a single model and allows us to control them, separately. One of the major advantage of the proposed AIA model is that efficient model fitting is achieved, whilst maintaining performance against illumination changes. In addition to model fitting, images illuminated from different directions can easily be synthesized by changing the parameters related to illumination modes. The method provides a practical approach, since only one image with frontal illumination of each person for training, is sufficient. There is no need to build complex models for illumination. As a result, this paper has presented a simple and efficient method for face modeling and face alignment in order to increase the performance of face localization by means of the proposed illumination invariant AIA method for face alignment, such as the Active Appearance Models, invariant to changes in illumination. From the experimental results, we showed that the proposed AIA model provides higher accuracy than classical Active Appearance Model for face alignment in a point-to-point error sense.