DeepFace

Abstract: Deep Neural Networks (DNNs) have established themselves as a dominant technique in machine learning DNNs have been top performers on a wide variety of tasks including image classification, speech recognition, and face recognition1-3 Convolutional neural networks (CNNs) have been used in nearly all of the top performing methods on the Labeled Faces in the Wild (LFW) dataset3-6 In this talk and accompanying paper, I attempt to provide a review and summary of the deep learning techniques used in the state-of-the-art In addition, I highlight the need for both larger and more challenging public datasets to benchmark these systems Despite the ability of DNNs and autoencoders to perform unsupervised feature learning, modern facial recognition pipelines still require domain specific engineering in the form of re-alignment For example, in Facebook's recent DeepFace paper, a 3D "frontalization" step lies at the beginning of the pipeline This step creates a 3D face model for the incoming image and then uses a series of affine transformations of the fiducial points to "frontalize" the image This step enables the DeepFace system to use a neural network architecture with locally connected layers without weight sharing as opposed to standard convolutional layers6 Deep learning techniques combined with large datasets have allowed research groups to surpass human level performance on the LFW dataset3, 5 The high accuracy (9963% for FaceNet at the time of publishing) and utilization of outside data (hundreds of millions of images in the case of Google's FaceNet) suggest that current face verification benchmarks such as LFW may not be challenging enough, nor provide enough data, for current techniques3, 5 There exist a variety of organizations with mobile photo sharing applications that would be capable of releasing a very large scale and highly diverse dataset of facial images captured on mobile devices Such an "ImageNet for Face Recognition" would likely receive a warm welcome from researchers and practitioners alike

Abstract: Machine learning (ML) has shown its impressive performance in the modern world, and many corporations leverage the technique of machine learning to improve their service quality, e.g., Facebook's DeepFace. Machine learning models with a collection of private data being processed by a training algorithm are deemed to be increasingly confidential. Confidential models are typically trained in a centralized cloud server but publicly accessible. ML-as-a-service (MLaaS) system is one of running examples, where users are allowed to access trained models and are charged on a pay-per-query basis. Unfortunately, recent researchers have shown the tension between public access and confidential models, where adversarial access to a model is abused to duplicate the functionality of the model or even learn sensitive information about individuals (known to be in the training dataset). We conclude these attacks as prediction API threats for simplicity. In this work, we propose ML defense, a framework to defend against prediction API threats, which works as an add-on to existing MLaaS systems. To the best of our knowledge, this is the first work to propose a technical countermeasure to attacks trumped by excessive query accesses. Our methodology neither modifies any classifier nor degrades the model functionality (e.g., rounds results). The framework consists of one or more simulators and one auditor. The simulator learns the hidden knowledge of adversaries. The auditor then detects whether there exists a privacy breach. We discuss the intrinsic difficulties and empirically state the efficiency and feasibility of our mechanisms in different models and datasets.

Abstract: Face recognition technology has made countless contributions in improving and changing the world. Attendance Systems utilizing Real-Time Face Recognition technology is a solution that can efficiently carry out the procedure of marking student attendance. Face recognition-based attendance system is the process by which we mark the attendance of the students present in the classroom by utilizing the facial data that is acquired from a surveillance camera. The proposed system captures the face of students attending the lecture by first detecting a face from the video input and with the help of an ensemble of deep learning models recognize the student and mark his/her attendance in the database [1]. This system uses an ensemble of facial recognition models such as VGG-FACE, Facenet, Openface, DeepFace so that it may be able to yield a much higher accuracy while identifying the subject.

Abstract: We use CNNs to build a system that both classifies images of faces based on a variety of different facial attributes and generates new faces given a set of desired facial characteristics. After introducing the problem and providing context in the first section, we discuss recent work related to image generation in Section 2. In Section 3, we describe the methods used to fine-tune our CNN and generate new images using a novel approach inspired by a Gaussian mixture model. In Section 4, we discuss our working dataset and describe our preprocessing steps and handling of facial attributes. Finally, in Sections 5, 6 and 7, we explain our experiments and results and conclude in the following section. Our classification system has 82\% test accuracy. Furthermore, our generation pipeline successfully creates well-formed faces.