 on the GEI, which is subsequently fed into the CNN which predicts the binary gender label. A sequential CNN setup first predicts the age group -Toddler (2-5), Child (6-11), Adolescent (12)(13)(14)(15)(16)(17)(18), Adult (19-60), Old (61-90) and subsequently the age classification CNN predicts the age of the subject.

Model Architecture
The proposed CNN architecture is depicted in figure 5. The input GEI of 128×88 pixels, is converted into an heatmap of 128×88 size. The proposed model includes of two pairs of consecutive convolution and max-pooling layers with a dropout probability of 0.5. The first pair has 81 filters of (5,5) and (3,3) max-pool filters and the second pair comprising of 45 filters of (7,7) and (3,3) max-pool filters. A dropout with probability 0.3 is applied beforehand the flattening operation. We used three fully connected layers with 1024, 256 and 32 nodes with a dropout rate of 0.3. The dense layers are initialized with the he_normal [20] as kernel_initializer and bias initialized to zeros. The relu [21] activation function is used in the three dense layers and sigmoid for the recognition layer. The architecture and hyperparameter selection were done through a rigorous training and testing of multiple architecturehyperparameters combinations and to arrive at the combination with best evaluation metrics results.

Model Training
We propose a two-phase training setup for the CNN. In the initial phase the model is compiled using the Adam optimizer with the default learning rate of 0.01 and categorical_crossentropy as the loss function in the first phase. It is trained for twenty epochs on the training set with a batch size of 32. The model checkpointing on val_accuracy ensures that only the model with best validation accuracy is saved. In the second phase the saved model is further trained in multiple subphases with iterative decreasing learning rate. The subsequent sub phases contain three epochs each and the learning rates iteratively decreasing by a factor of 10 -3, 0.8, 0.2, 0.08 respectively.

Dataset
The model was evaluated on the OU-ISIR Gait Database, Large Population Dataset with Age (OULP-Age), which is the largest Gait database developed till date. The OU-ISIR biometric database is a repository of various Gait databases like the Large Population Dataset with Age, Population Dataset with Bag, Inertial Sensor Dataset, etc. They were developed by capturing the side view video of Gait sequence of each subject followed by a three step GEI extraction: segmentation, normalization and averaging. The Large Population Dataset with Age is a collection of 63846 GEI images of both male and female with ages between 2 and 90 years.   Table 2 gives the gender wise breakup of the database. The training settest set split is done at 50% (15961 and 15962 subjects respectively). The split remains the same for all the three classifications processes.

Performance Evaluation
Since the gender estimation is a bi class classification problem thus, we require a simple evaluation metric like the accuracy, which is given by where Nc represents the total number of samples that were accurately classified by the model and N gives the total sample size. In age estimation problem (a regression problem), the frequently used performance evaluation metrics are the Mean Absolute Error (MEA) and Standard Deviation (SD) which are given by the following equations: Where N is the total sample size of the test set, tx and px are the actual and predicted values of age for the x th sample.
The following formula is used to compute the standard deviation:   The same CNN architecture when employed for age group estimation yielded the following classification results as shown in Table 7 and 8.

GaitNet with heatmap filtered GEI:
We improved the gender and age group classification accuracy of the conventional single dense layer CNN with our proposed GaitNet. The training set and test set gender classification accuracy reached up to 99.03% and 96.96% respectively. Table 9 and 10 depict the gender classification results of GaitNet with heatmap filtered GEI over training and test set respectively. Table 11 and 12 present the confusion matrix for age group prediction over the training set and test set respectively.

GaitNet with image augmentation:
The inherent class imbalance problem in the OULP dataset induces lower classification accuracy for toddler, child and old age groups. We used image augmentation technique over these two age groups to alleviate the problem. To increase the number of subjects in the scarce age groups we cloned the GEI's in the training set of toddler, child and old age groups. We employed the width_shift and height_shift transformations on the enlarged dataset. The size of the augmented dataset increased up to 76,667 subjects. We can use the adding of lower levels of curated noise for data augmentation such that the new images generated could preserve the discriminating features and at the same time generate new subjects.

Age estimation results using the GaitNet:
The GaitNet employs a sequential process for the age estimation. First the age group of the subject is predicted followed by age estimation using a sequential CNN trained over the predicted age group. Instead of addressing the age estimation problem as a regression problem we considered it as a classification problem. So, the number of nodes in the final layer of GaitNet are equal to the number of distinct age values in the respective age group, e.g., the CNN for age prediction for toddler age group has 4 nodes as it has age values 2-5 years. Table 14 gives the comparative description of our method with the existing methods in the literature using the MEA and SD evaluation metrics. Figure  7 plots the true age values against the predicted values.

V. CONCLUSION AND FUTURE SCOPE
In this paper we proposed GaitNet to improve the Gaitbased age estimation accuracy and it outperformed all other existing Gait-based age estimation models. The heatmap filter assisted in making it possible to train a deeper CNN by increasing the number of distinguishing features in the GEI. The image augmentation technique alleviated the implications of data scarcity of elder and child subjects. Though we addressed most of the shortcomings of the existing models but some still continue to prevail like the sequential nature of the age prediction process connotes that wrong age group prediction for a subject would subsequently degrade its age prediction.
In future the work can be extended by further feature enhancement of the GEI Gait descriptor eventually making employing a deeper CNN a possibility. The development of OULP Gait database by addition of more subjects in the elder and children age group would further enhance the age group prediction accuracy ultimately improving the age estimation accuracy.THE PLACE: Since its construction in 1878, this building has housed the Dominion Brewery, the CBC’s prop warehouse and some retail shops. The conversion to residential units in 1999 resulted in an edgy yuppie’s (a yedgie’s?) ideal vision of an industrial loft. Edgy for the wide hallways, 14-foot ceilings, exposed pipes and concrete pillars. Yuppie for the double-showered bathroom, black-and-chrome kitchen, window-heavy studio and large office.
BRAGGING RIGHTS: There’s still street cred to be had here, as Corktown has not reached the gleaming condoization of the Distillery District and Leslieville (although it’s no coincidence that those ‘hoods are close by). In ten years, the sentence “Well, I moved here in 2010” might carry some weight.
BIGGEST SELLING POINT: This loft is only going to become more attractive as developers turn their gaze to the last untouched section of Queen. After all, the best bits of the east end are just minutes away, with Leslieville, Cabbagetown, the Beach and the Distillery District surrounding the Brewery Lofts’ less-developed enclave.
POSSIBLE DEAL BREAKER: Who wants to stack their bicycles in their front hall?
Where are the windows in the condo?
If the sq ft is correct, this loft is a steal @ just about $400 per sq ft. Wonder what the maint fee is… nice layoout..the minimalistic approach is great, of course maybe it was a staging! Good luck on the sale!
$800k is pretty pricey. Especially for the edges of Regent Park.
Slight correction – the Brewery Lofts, despite the name, is solely a repurposed 1950s era CBC prop warehouse. Never a brewery. The Dominion Brewery is an adjecent (but completely seperate) building that dates to the 1870s and was reno’d extrnsively in the late 1980s.
There are not many opportunities to own 2000+ sqft for $800K downtown, let alone with a finish as fantastic as this. I bet the original buyers circa 1999 have made a killing. Sign me up![Q]: "She was a member of that camp when her husband was lost trying to take supplies and new sleds to Stefansson in the winter of 1916." --> French?
[A]: Jennie fait partie de ce camp lorsque son mari périt en tentant d'apporter les provisions et de nouveaux traîneaux à Stefansson à l'hiver 1916.

[Q]: "The costs for the required rent-free office space of approximately US$ 54,000 yearly are already taken into account in the operational budget for 2006-2007 adopted by the Conference at its first meeting." --> French?
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