Publication Date:
2013
Short description:
Short-and long-time ageing effects in face recognition / Tistarelli, M., Yadav, D., Vatsa, M., Singh, R.. - (2013), pp. 253-275. [10.1049/PBSP010E_ch14]
abstract:
The flow of time, either in the short or in the long, brings significant variations in human faces
which often make it more difficult to perform face recognition. In formative years, the change in shape
and size in the structure of the face is considerable, whereas after the age of 18 years, mainly the texture
of the face changes. In addition to this, we can distinguish between two kinds of aging effects: short time
or transient changes, and long-time or permanent changes. The former is experienced when comparing
the face appearance of the same subject at close time instants and it can be due to a number of changes
in the body as well as in the environment. The latter is the change in the facial structure due to the
aging or growth/shrink of the tissues. As a consequence, it becomes a tough task to identify a subject by
comparing face images sampled at different times. This chapter analyzes the effects of both short-time
and long-time aging on face images and tries to localize the variations in appearance and to quantify the
errors in recognition. The developed framework for short-time or transient aging analysis, exploits the
concept of distinctiveness of facial features and its temporal evolution. The analysis is performed both
at a global and local level to define which facial features are more stable over time. Several experiments
are performed on publicly available databases with image sequences densely sampled over a time span
of several years.
In order to evaluate computational models to either detect or compensate for long-time aging effects,
a novel face dataset has been purposively collected, composed of 102 individuals with over 2600 images with age separation and other variations including illumination, pose, and eyeglasses. The purpose of
this newly acquired dataset, named Delhi face aging database, is to objectively determine the variations
in recognition accuracy due to aging, in real life conditions. In order to facilitate a comparative analysis
of different algorithms, all images are annotated. Different algorithms are described and their accuracies
are compared.
which often make it more difficult to perform face recognition. In formative years, the change in shape
and size in the structure of the face is considerable, whereas after the age of 18 years, mainly the texture
of the face changes. In addition to this, we can distinguish between two kinds of aging effects: short time
or transient changes, and long-time or permanent changes. The former is experienced when comparing
the face appearance of the same subject at close time instants and it can be due to a number of changes
in the body as well as in the environment. The latter is the change in the facial structure due to the
aging or growth/shrink of the tissues. As a consequence, it becomes a tough task to identify a subject by
comparing face images sampled at different times. This chapter analyzes the effects of both short-time
and long-time aging on face images and tries to localize the variations in appearance and to quantify the
errors in recognition. The developed framework for short-time or transient aging analysis, exploits the
concept of distinctiveness of facial features and its temporal evolution. The analysis is performed both
at a global and local level to define which facial features are more stable over time. Several experiments
are performed on publicly available databases with image sequences densely sampled over a time span
of several years.
In order to evaluate computational models to either detect or compensate for long-time aging effects,
a novel face dataset has been purposively collected, composed of 102 individuals with over 2600 images with age separation and other variations including illumination, pose, and eyeglasses. The purpose of
this newly acquired dataset, named Delhi face aging database, is to objectively determine the variations
in recognition accuracy due to aging, in real life conditions. In order to facilitate a comparative analysis
of different algorithms, all images are annotated. Different algorithms are described and their accuracies
are compared.
Iris type:
2.1 Contributo in volume (Capitolo o Saggio)
Keywords:
Face recognition; Age estimation; Biometrics
List of contributors:
Tistarelli, Massimo; Yadav, D; Vatsa, M; Singh, R.
Book title:
Age Factors in Biometric Processing