Multi-view Face Detection Using Deep Convolutional Neural Networks

In this paper we consider the problem of multi-view face detection. While there has been significant research on this problem, current state-of-the-art approaches for this task require annotation of facial landmarks, e.g. TSM [25], or annotation of face poses [28, 22]. They also require training dozens of models to fully capture faces in all orientations, e.g. 22 models in HeadHunter method [22]. In this paper we propose Deep Dense Face Detector (DDFD), a method that does not require pose/landmark annotation and is able to detect faces in a wide range of orientations using a single model based on deep convolutional neural networks. The proposed method has minimal complexity; unlike other recent deep learning object detection methods [9], it does not require additional components such as segmentation, bounding-box regression, or SVM classifiers. Furthermore, we analyzed scores of the proposed face detector for faces in different orientations and found that 1) the proposed method is able to detect faces from different angles and can handle occlusion to some extent, 2) there seems to be a correlation between dis- tribution of positive examples in the training set and scores of the proposed face detector. The latter suggests that the proposed methods performance can be further improved by using better sampling strategies and more sophisticated data augmentation techniques. Evaluations on popular face detection benchmark datasets show that our single-model face detector algorithm has similar or better performance compared to the previous methods, which are more complex and require annotations of either different poses or facial landmarks.Comment: in International Conference on Multimedia Retrieval 2015 (ICMR

HAPI: Hardware-Aware Progressive Inference

Convolutional neural networks (CNNs) have recently become the state-of-the-art in a diversity of AI tasks. Despite their popularity, CNN inference still comes at a high computational cost. A growing body of work aims to alleviate this by exploiting the difference in the classification difficulty among samples and early-exiting at different stages of the network. Nevertheless, existing studies on early exiting have primarily focused on the training scheme, without considering the use-case requirements or the deployment platform. This work presents HAPI, a novel methodology for generating high-performance early-exit networks by co-optimising the placement of intermediate exits together with the early-exit strategy at inference time. Furthermore, we propose an efficient design space exploration algorithm which enables the faster traversal of a large number of alternative architectures and generates the highest-performing design, tailored to the use-case requirements and target hardware. Quantitative evaluation shows that our system consistently outperforms alternative search mechanisms and state-of-the-art early-exit schemes across various latency budgets. Moreover, it pushes further the performance of highly optimised hand-crafted early-exit CNNs, delivering up to 5.11x speedup over lightweight models on imposed latency-driven SLAs for embedded devices.Comment: Accepted at the 39th International Conference on Computer-Aided Design (ICCAD), 202

Personnel recognition and gait classification based on multistatic micro-doppler signatures using deep convolutional neural networks

In this letter, we propose two methods for personnel recognition and gait classification using deep convolutional neural networks (DCNNs) based on multistatic radar micro-Doppler signatures. Previous DCNN-based schemes have mainly focused on monostatic scenarios, whereas directional diversity offered by multistatic radar is exploited in this letter to improve classification accuracy. We first propose the voted monostatic DCNN (VMo-DCNN) method, which trains DCNNs on each receiver node separately and fuses the results by binary voting. By merging the fusion step into the network architecture, we further propose the multistatic DCNN (Mul-DCNN) method, which performs slightly better than VMo-DCNN. These methods are validated on real data measured with a 2.4-GHz multistatic radar system. Experimental results show that the Mul-DCNN achieves over 99% accuracy in armed/unarmed gait classification using only 20% training data and similar performance in two-class personnel recognition using 50% training data, which are higher than the accuracy obtained by performing DCNN on a single radar node

Evaluation of sleep stage classification using feature importance of EEG signal for big data healthcare

Sleep analysis is widely and experimentally considered due to its importance to body health care. Since its sufficiency is essential for a healthy life, people often spend almost a third of their lives sleeping. In this case, a similar sleep pattern is not practiced by every individual, regarding pure healthiness or disorders such as insomnia, apnea, bruxism, epilepsy, and narcolepsy. Therefore, this study aims to determine the classification patterns of sleep stages, using big data for health care. This used a high-dimensional FFT extraction algorithm, as well as a feature importance and tuning classifier, to develop accurate classification. The results showed that the proposed method led to more accurate classification than previous techniques. This was because the previous experiments had been conducted with the feature selection model, with accuracy implemented as a performance evaluation. Meanwhile, the EEG Sleep Stages classification model in this present report was composed of the feature selection and importance of the extraction stage. The previous and present experiments also reached the highest values of accuracy, with the Random Forest and SVM models using 2000 and 3000 features (87.19% and 89.19%, respectively. In this article, we proposed an analysis that the feature importance subsequently influenced the model's accuracy. This was because the proposed method was easily fine-tuned and optimized for each subject to improve sensitivity and reduce false negative occurrences

Incremental class representation learning for face recognition

Image classification is one of the most active challenging problems in computer vision field. Taking this to Deep Neural Networks with systems that are able to deal with large data sets as it can be ImageNet. Large Convolutional Networks as VGG-16 used in this work have recently demonstrated impressive classification performances. This work is focused on novel techniques for Incremental Learning stages for face recognition, which is an important open problem in artificial intelligence. The main challenge of this work is the development of incrementally learning systems that learn about more and more concepts over time. Most of the actual methods that use incremental learning in "online" or "offline" stages. This thesis focuses on "offline" incremental stages where the data available is distributed in batches of classes. Since the necessity to deal with a continuous training stages, some well-established methods for transfer learning are applied by the author to run the experiments. Preserving knowledge is the most challenge task to deal using incremental learning techniques. The actual research is on apply incremental learning in natural systems where for example, it is not considered to store all the old training data to make a new model when new data comes available. Another interesting concept for incremental learning systems is "lifelong" learning, which are related to the methods analyzed in this work since the system proposed also learn from a sequence of different tasks. The similarity of multi-task learning and "lifelong" learning is that they both use shared information across tasks to help learning, but also, multi-task learning is not able to grow the number of tasks over time preserving the knowledge.La clasificación de imágenes es una de las tareas más desafiantes en el campo de la visión por computador. Llevando esto al campo de las redes neuronales profundas utilizando sistemas que son capaces de gestionar datasets considerablemente grandes como puede ser ImageNet. Grandes redes convolucionales cómo puede ser VGG-16, que és la que se utilizarà en este trabajo, han demostrado muy buenos resultados. Este trabajo està focalizado en nuevas técnicas para aprendizaje incremental para el reconocimiento de caras, que \'{e}s un importante problema abierto en la inteligencia artificial. El mayor reto en este trabajo consiste en desarrollar dos sistemas incrementales que aprenden más conceptos a medida que pasa el tiempo. Muchos de estos métodos que utilizan el aprendizaje incremental en escenarios como "online" o "offline". Este trabajo está focalizado sobre todo en los sistemas incrementales que utilizan "offline" como método incremental de aprendizaje donde los datos son proporcionados por conjuntos separados de classes. Hay una necesidad clara de gestionar escenarios de aprendizaje continuo, y es por este motivo que métodos de transferencia de aprendizaje han estado estudiados y implementados por el autor del proyecto para tal de llevar a cabo la ejecución de experimentos. Una de las tascas más desafiantes es cómo gestionar y preservar el conocimiento obtenido para no olvidar. Cuando se habla de aprendizaje incremental, muchas veces va relacionado con el concepto de sistemas naturales donde por ejemplo, no esté contemplada la opci\'{o}n todas las muestras para el conocimiento adquirido para un futuro entrenamiento cuando haya clases disponibles para hacerlo. En cambio, el aprendizaje "online", se diferencia del "offline" durante el proceso de entrenamiento. Dónde se encarga de aprender de forma eficiente con datos que llegan de una forma incremental pero siempre corresponden a las mismas clases, dicho de otro modo, los sistemas que utilizan el aprendizaje "online" en la mayoría de trabajos propuestos, no se encargan de incrementar el nombre de clases. Otro concepto interesante para los sistemas de aprendizaje incremental es lo que se llama aprendizaje "lifelong", que también está relacionado con los métodos analizados en este trabajo, ya que el sistema propuesto también aprende de una secuencia de tascas distintas. También hay una similitud entre el aprendizaje para múltiples tascas y el aprendizaje "lifelong", que es que los dos métodos utilizan información compartida entre tascas para ayudar en el aprendizaje, de todas formas, los sistemas de aprendizaje para múltiples tascas tampoco puede augmentar el nombre de clases.La classificació d'imatges és una de les tasques més desafiants en el camp de la visi\'{o} per a computadors. Portant això al camp de les xarxes neuronals profundes utilitzant sistemes que s\'{o}n capa\c{c}os de gestionar datasets considerablement grans, com pot ser el de ImageNet. Grans xarxes convolucionals com pot ser VGG-16, que \'{e}s la que s'utilitzarà en aquest treball i que ha demostrat molt bons resultats. Aquest treball està focalitzat en noves tàcniques per aprenentatge incremental per al reconeixament de cares, que és un important problema obert en la inteligència artificial. El major repte en aquest treball és desenvolupar dos sistemes incrementals que aprenen més conceptes durant el temps. Molts dels actuals mètodes que utilitzen l'aprenentatje incrmental en escenaris com "online" o "offline". Aquest treball està focalitzat sobretot en els sistemes incrementals que utilitzen "offline" com a mètode incremental d'aprenentatge on les dades són proporcionades per conjunts de classes, on cada conjunt apareix en un moment diferent. Hi ha una necessitat per a gestionar amb escenaris d'aprenentatge continuu, i \'{e}s per això que mètodes de tranferència d'aprenentatje serán estudiats i implementats per l'autor del projecte per tal d'executar els experiments. Una de les tasques més desafiants és com gestionar i preservar el coneixament obtingut per tal de no oblidar. Quan es parla de aprenentatje incremental, molts cops està relacionat amb el concepte de sistemes naturals on per exemple, no està contemplada la possibilitat de guardar totes les mostres del coneixament adquirit per a un futur entrenament quan hi hagin noves classes disponibles. D'altra banda, l'aprenentatge "online" es diferencia del "offline" durant el procés d'entrenament. On s'encarrega d'apendre de forma eficient amb dades que arriben de forma incremental però sempre per les mateixes tasques, dit d'altre forma, els sistemes que utilitzen l'aprenentatge "online" en la majoria de treballs proposats, no s'encarreguen d'incrementar el nombre de classes. Un altre concepte interessant per als sistemes d'aprenentatge incremental és el que se'n diu aprenentatge lifelong, que també està relacionat amb els mètodes analitzats en aquest treball, ja que el sistema proposat també aprèn d'una seqüència de tasques diferents. També hi ha una similaritat entre l'aprenentatge per múltiples tasques i l'aprenentatge "lifelong", que és que els dos utilitzen informació compartida entre tasques per ajudar en l'aprenentatge, de totes formes, els sistemes d'aprenentatge per a múltiples tasques tampoc poden augmentar el nombre de classes

iTeleScope: Intelligent Video Telemetry and Classification in Real-Time using Software Defined Networking

Video continues to dominate network traffic, yet operators today have poor visibility into the number, duration, and resolutions of the video streams traversing their domain. Current approaches are inaccurate, expensive, or unscalable, as they rely on statistical sampling, middle-box hardware, or packet inspection software. We present {\em iTelescope}, the first intelligent, inexpensive, and scalable SDN-based solution for identifying and classifying video flows in real-time. Our solution is novel in combining dynamic flow rules with telemetry and machine learning, and is built on commodity OpenFlow switches and open-source software. We develop a fully functional system, train it in the lab using multiple machine learning algorithms, and validate its performance to show over 95\% accuracy in identifying and classifying video streams from many providers including Youtube and Netflix. Lastly, we conduct tests to demonstrate its scalability to tens of thousands of concurrent streams, and deploy it live on a campus network serving several hundred real users. Our system gives unprecedented fine-grained real-time visibility of video streaming performance to operators of enterprise and carrier networks at very low cost.Comment: 12 pages, 16 figure