Application of Multiscale Facial Feature Manifold Learning Based on VGG-16

Purpose. In order to solve the problems of small face image samples, high size, low structure, no label, and difficulty in tracking and recapture in security videos, we propose a popular multiscale facial feature manifold (MSFFM) algorithm based on VGG16. Method. We first build the VGG16 architecture to obtain face features at different scales and construct a multiscale face feature manifold with face features at different scales as dimensions. At the same time, the recognition rate, accuracy rate, and running time are used to evaluate the performance of VGG16, LeNet-5, and DenseNet on the same database. Results. From the results of comparative experiments, it can be seen that the recognition rate and accuracy of VGG16 are the highest among the three networks. The recognition rate of VGG16 is 97.588%, and the accuracy is 95.889%. And the running time is only 3.5 seconds, which is 72.727% faster than LeNet-5 and 66.666% faster than DenseNet. Conclusion. The model proposed in this paper breaks through the key problem in the face detection and tracking problem in the public security field, predicts the position of the face target image in the time dimension manifold space, and improves the efficiency of face detection.

Layer-specific ultra-high field 7T fMRI showing that threat anticipation is mediated by the pulvinar input to the superficial layers of primary visual cortex (V1)

The perceptual system gives priority to threat-relevant signals with survival value. Its mechanism may not only include the processing initiated in the presence of threat signals but also in the mere anticipation of such signals. Here, we show that the pulvinar modulates activity in the early visual cortex (V1) specifically in threat anticipation. Using ultra-high-field 7T fMRI, we examined the layer-specific interaction between V1 and the pulvinar, while taking advantage of the fact that the directionality of such interaction is anatomically constrained in specific V1 layers. Only in anticipation of a fearful face target, but not of a control happy face target, was false perception of anticipated-yet-unpresented target face accompanied by stronger activity in the V1 superficial-cortical-depth (layers 1-3), which was preceded by pre-target-onset pulvinar activity. The pulvinar may contribute to the visual processing initiated in the anticipation of threat, and play an important role in anxiety.

A Bottleneck in Face Identification

There is evidence that face processing is capacity-limited in distractor interference tasks and in tasks requiring overt recognition memory. We examined whether capacity limits for faces can be observed with a more sensitive measure of visual processing, by measuring repetition priming of flanker faces that were presented alongside a face or a nonface target. In Experiment 1, we found identity priming for face flankers, by measuring repetition priming across a change in image, during task-relevant nonface processing, but not during the processing of a concurrently-presented face target. Experiment 2 showed perceptual priming of the flanker faces, across identical images at prime and test, when they were presented alongside a face target. In a third Experiment, all of these effects were replicated by measuring identity priming and perceptual priming within the same task. Overall, these results imply that face processing is capacity limited, such that only a single face can be identified at one time. Merely attending to a target face appears sufficient to trigger these capacity limits, thereby extinguishing identification of a second face in the display, although our results demonstrate that the additional face remains at least subject to superficial image processing.