Facegen Additional Hair Models Download
LINK ->>> https://shoxet.com/2sBJPh
The goal is to capture, combine, and merge the features of the state-of-the-art face datasets above with the features of FaceGen. Specifically, we used nine sets of male and female faces from FaceGen (or from one of its predecessor projects). FaceGen uses a database of laser-scanned male and female human faces to create new faces.
Traditional face perception experiments typically use two-dimensional images obtained from actors whose identities are unknown to the participant. Such images do not have the defined features and textures of the models described above. These experiments are nonetheless subject to the same limitations as studies of real-world facial stimuli. Namely, the unidentified actors differ from a particular participant's norms for the relevant features, such as the width of the neck. This \"norm confound\"  means that variability in an actor's neck width may either facilitate or hinder perception of a particular trait. The other example limitation concerns the lack of control over other features, such as facial hair, make-up, and generally what participants perceive as a \"true to life\" identity of the actor. Last, this limitation pertains even to images with defined features such as a Tube Face. In such images, a person's identity is not always easily discernible, so any given face can be misidentified as another if it appears to have features that are too dissimilar. The use of face stimuli such as those described above is a step toward overcoming these limitations.
The computer-generated faces contain defined features and textures, and they allow us to tightly control the face stimuli for each participant. In addition, the images can be grouped according to identity and sex. For example, we can have many faces for each identity and classify the faces by sex (e.g., all faces with female identity and male sex). Using machine learning techniques that we describe below, we can train classifiers that can then reliably distinguish the stimuli by sex and by identity. In other words, a computer-trained classifier can be used to sort a large set of faces according to sex and identity, in order to identify sets of faces that are predicted to be similar to each other with respect to a specific trait. 7211a4ac4a