Face To Face With Race Pdf Download ((EXCLUSIVE))
Download File > https://bltlly.com/2tcH1u
Both Yu et al. and Wang et al. are able to effectively reduce the bias between the 0-indexed ethnicity labels of Chinese and American as measured by the cosine distance between the vectors. However, these distortions tend to go in both directions: for different ethnicities (Caucasian to Asian and vice versa), as well as for females and males [41], [43]. Although somewhat unrelated, a recent study on age estimation showed that the embeddings of older (above 40) facial images are more similar to younger images (below 20) than the other way around, pointing to the danger of implicitly transferring age stereotypes [44].
Pereira and Alvarez tested the model robustness against amounts of missing pixels on the label and on the image [45]. Their tests showed that a missing pixels of the label (missing pixels of corresponding ethnicities) are more critical than the removal of pixels within the image (missing pixels or equal pixels of the corresponding ethnic labels, e.g., Caucasian). Furthermore, they showed that the robustness against label noise is similar for European and Asian faces, whereas African faces are the most susceptible to it.
Different approaches to enlarge the training set have been explored, e.g., the combination of in-the-wild faces with high-quality faces taken from the web [46], or the use of synthetic images to adaptively enrich the training set [47].
Suk et al. analyzed the sensitivity of embedding vectors to the position of a given feature [43]. They showed that the vectors of the facial features (nose, eyes, mouth) as well as the position of those features can be used as sensitive attributes. The embedding vectors of these features correspond to biases in face recognition systems. In particular, eyes, nose, and mouth constitute the area of interest of facial recognition systems. The embedding vectors corresponding to these features are distinctive in a way that the angle of the vectors indicates the position of the feature. The embedding vectors of the eyes and nose point to the central illumination of a face, while the mouth points to the left and right sides. d2c66b5586