Photo morph age progression applications

#Photo morph age progression applications skin

Unlike existing contrastive learning methods, which are separated from the downstream tasks, GACN integrates contrastive learning into age regression and jointly optimizes them for age representation learning. Thus, faces belonging to the same age group are pulled together, while clusters of faces from different age groups are pushed apart. Specifically, with a carefully designed contrastive loss function, GACN minimizes intraвЂђclass distances and maximizes interвЂђclass distances between different age groups in feature space. This paper proposes a GroupвЂђaware Contrastive Network (GACN), a robust lightweight model, which extracts discriminative features by leveraging contrastive learning rather than increasing model parameters.

#Photo morph age progression applications skin

For operational scenarios where image acquisition is controlled, we propose acquiring images with lighting adjusted to yield face skin brightness in a narrow range.ДЄlthough great efforts have been devoted to developing lightweight models for age estimation in recent works, the robustness is still unsatisfactory in unconstrained environments. Using a brightness information metric that captures the variation in brightness in the face skin region, the variation in matching accuracy is shown to correlate with the level of information available in the face skin region. Image pairs with strongly different face skin brightness are found to have decreased FMR and increased false non-match rate (FNMR).

Image pairs with both images having mean face skin brightness that is too dark or too light are found to have an increased false match rate (FMR). Image pairs with both images having mean face skin brightness in an upper-middle range of brightness are found to have the highest matching accuracy across demographics and matchers. This is the first work that we are aware of to explore how the level of brightness of the skin region in a pair of face images impacts face recognition accuracy. To promote reproducible research, matchers, attribute classifiers, and datasets used in this research are/will be publicly available. These results suggest that future research on demographic variation in accuracy should include a check for balanced quality of the test data as part of the problem formulation. We show this result for two different matchers, and analyzing images of Caucasians and of African-Americans. We then demonstrate that when the data used to estimate recognition accuracy is balanced across gender for how hairstyles occlude the face, the initially observed gender gap in accuracy largely disappears. In particular, compared to females, male facial hair contributes to creating a greater average difference in appearance between different male faces. We first demonstrate that female and male hairstyles have important differences that impact face recognition accuracy. Even the recent NIST report on demographic effects lists 'analyze cause and effect' under 'what we did not do'. However, relatively little is known about the cause(s) of this gender gap. It is broadly accepted that there is a 'gender gap' in face recognition accuracy, with females having higher false match and false non-match rates. Extensive experiments on UDA medical diagnoses and facial age estimation demonstrate its effectiveness. We explicitly disentangle the cross-domain images into a shared ordinal prior induced ordinal content space and two separate source/target ordinal-unrelated spaces, and the self-training is worked on the shared space exclusively for ordinal-aware domain alignment. Furthermore, we are able to control the density of content vectors that violate the poset constraint by a simple 'three-sigma rule'. Gaussian latent prior, in this work, a recursively conditional Gaussian (RCG) set is proposed for ordered constraint modeling, which admits a tractable joint distribution prior. Target for this, the partially ordered set (poset) is defined for constraining the latent vector. The UDA for ordinal classification requires inducing non-trivial ordinal distribution prior to the latent space. However, in many tasks (e.g., medical diagnosis), the labels are discrete and successively distributed. There has been a growing interest in unsupervised domain adaptation (UDA) to alleviate the data scalability issue, while the existing works usually focus on classifying independently discrete labels.