Utilizing CNNs and transfer learning of pre-trained models for age range classification from unconstrained face images

Mallouh, Arafat Abu; Qawaqneh, Zakariya; Barkana, Buket D.

doi:10.1016/j.imavis.2019.05.001

Cited by 38 publications

(17 citation statements)

References 15 publications

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“…The first convolutional layer filters the n × 6 × 1 input acceleration data with 64 kernels of size 3×1 and stride 1. The L2 regularization technique is used in this layer with a weight decay coefficient of 0.01 (Mallouh et al, 2019). After the first convolutional layer a zero-padding is used such that the output has the same length as the original input.…”

Section: Machine Learning Modelmentioning

confidence: 99%

Automatic equine activity detection by convolutional neural networks using accelerometer data

Eerdekens

Deruyck

Fontaine

et al. 2020

Computers and Electronics in Agriculture

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In recent years, with a widespread of sensors embedded in all kind of mobile devices, human activity analysis is occurring more often in several domains like healthcare monitoring and fitness tracking. This trend did also enter the equestrian world because monitoring behaviours can yield important information about the health and welfare of horses. In this research, a deep learning-based approach for activity detection of equines is proposed to classify seven activities based on accelerometer data. We propose using Convolutional Neural Networks (CNN) by which features are extracted automatically by using strong computing capabilities. Furthermore, we investigate the impact of the sampling frequency, the time series length and the type of underground on which the data is gathered on the recognition accuracy and evaluate the model on three types of experimental datasets that are compiled of labelled accelerometer data gathered from six different subjects performing seven different activities. Afterwards, a horse-wise cross validation is carried out to investigate the impact of the subjects themselves on the model recognition accuracy. Finally, a slightly adjusted model is validated on different amounts of 50 Hz sensor data. A 99% accuracy can be reached for detecting seven behaviours of a seen horse when the sampling rate is 25 Hz and the time interval is 2.1 s. Four behaviours of an unseen horse can be detected with the same accuracy when the sampling rate is 69 Hz and the time interval is 2.4 s. Moreover, the accuracy of the model for the three datasets decreased on average with about 4.75% when the sampling rate was decreased from 200 Hz to 25 Hz and with 5.27% when the time interval was decreased from 3 s to 0.6 s. In addition, the classification performance of the activity "walk" was not influenced by the type of underground the horse was performing this movement on and even the model could conclude from which underground the data was gathered for three out of four undergrounds with accuracies above 93% at time intervals higher than 1.2 s. This ensures the evaluation of activity patterns in real world circumstances. The performance and ability of the model to generalise is validated on 50 Hz data from different horse types, using tenfold cross validation, reaching a mean classification accuracy of 97.84% and 96.10% when validated on a lame horse and pony, respectively. Moreover, in this work we show that using data from one sensors is at the cost of only 0.24% reduction in accuracy (99.42% vs 99.66%).

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Section: Machine Learning Modelmentioning

confidence: 99%

Automatic equine activity detection by convolutional neural networks using accelerometer data

Eerdekens

Deruyck

Fontaine

et al. 2020

Computers and Electronics in Agriculture

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“…The stride indicates the number of steps by which the filter moves over the data. The L2 regularization technique is used in this layer with a weight decay coefficient of 0.01 (Mallouh et al, 2019). The first convolutional layer is followed by a zeropadding and max-pooling operation.…”

Section: Algorithms For Behavior Classificationmentioning

confidence: 99%

A framework for energy-efficient equine activity recognition with leg accelerometers

Eerdekens

Deruyck

Fontaine

et al. 2021

Computers and Electronics in Agriculture

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“…Ensuring a substantial amount of data, the model was expected to be invulnerable regarding its classification capabilities due to the input’s data variations. In addition, the parametric nature of the model [ 29 ] meant that the concept of transferred learning could be applied according to the envisioned training process [ 30 , 31 ].…”

Section: Approachmentioning

confidence: 99%

Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase

Papacharalampopoulos

Tzimanis

Sabatakakis

et al. 2020

Sensors

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Vision technologies are used in both industrial and smart city applications in order to provide advanced value products due to embedded self-monitoring and assessment services. In addition, for the full utilization of the obtained data, deep learning is now suggested for use. To this end, the current work presents the implementation of image recognition techniques alongside the original the quality assessment of a Parabolic Trough Collector (PTC) reflector surface to locate and identify surface irregularities by classifying images as either acceptable or non-acceptable. The method consists of a three-step solution that promotes an affordable implementation in a relatively small time period. More specifically, a 3D Computer Aided Design (CAD) of the PTC was used for the pre-training of neural networks, while an aluminum reflector surface was used to verify algorithm performance. The results are promising, as this method proved applicable in cases where the actual part was manufactured in small batches or under the concept of customized manufacturing. Consequently, the algorithm is capable of being trained with a limited number of data.

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Utilizing CNNs and transfer learning of pre-trained models for age range classification from unconstrained face images

Cited by 38 publications

References 15 publications

Automatic equine activity detection by convolutional neural networks using accelerometer data

Automatic equine activity detection by convolutional neural networks using accelerometer data

A framework for energy-efficient equine activity recognition with leg accelerometers

Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase

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